Reference documentation for deal.II version 9.5.0
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step-14.h
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1) const
744 *   {
745 *   std::ofstream out(output_name_base + "-" +
746 *   std::to_string(this->refinement_cycle) + ".svg");
747 *   GridOut().write_svg(dof_handler.get_triangulation(), out);
748 *   }
749 *   } // namespace Evaluation
750 *  
751 *  
752 * @endcode
753 *
754 *
755 * <a name="TheLaplacesolverclasses"></a>
756 * <h3>The Laplace solver classes</h3>
757 *
758
759 *
760 * Next are the actual solver classes. Again, we discuss only the
761 * differences to the previous program.
762 *
763 * @code
764 *   namespace LaplaceSolver
765 *   {
766 * @endcode
767 *
768 *
769 * <a name="TheLaplacesolverbaseclass"></a>
770 * <h4>The Laplace solver base class</h4>
771 *
772
773 *
774 * This class is almost unchanged, with the exception that it declares two
775 * more functions: <code>output_solution</code> will be used to generate
776 * output files from the actual solutions computed by derived classes, and
777 * the <code>set_refinement_cycle</code> function by which the testing
778 * framework sets the number of the refinement cycle to a local variable
779 * in this class; this number is later used to generate filenames for the
780 * solution output.
781 *
782 * @code
783 *   template <int dim>
784 *   class Base
785 *   {
786 *   public:
787 *   Base(Triangulation<dim> &coarse_grid);
788 *   virtual ~Base() = default;
789 *  
790 *   virtual void solve_problem() = 0;
791 *   virtual void postprocess(
792 *   const Evaluation::EvaluationBase<dim> &postprocessor) const = 0;
793 *   virtual void refine_grid() = 0;
794 *   virtual unsigned int n_dofs() const = 0;
795 *  
796 *   virtual void set_refinement_cycle(const unsigned int cycle);
797 *  
798 *   virtual void output_solution() const = 0;
799 *  
800 *   protected:
802 *  
803 *   unsigned int refinement_cycle;
804 *   };
805 *  
806 *  
807 *   template <int dim>
808 *   Base<dim>::Base(Triangulation<dim> &coarse_grid)
809 *   : triangulation(&coarse_grid)
810 *   , refinement_cycle(numbers::invalid_unsigned_int)
811 *   {}
812 *  
813 *  
814 *  
815 *   template <int dim>
816 *   void Base<dim>::set_refinement_cycle(const unsigned int cycle)
817 *   {
818 *   refinement_cycle = cycle;
819 *   }
820 *  
821 *  
822 * @endcode
823 *
824 *
825 * <a name="TheLaplaceSolverclass"></a>
826 * <h4>The Laplace Solver class</h4>
827 *
828
829 *
830 * Likewise, the <code>Solver</code> class is entirely unchanged and will
831 * thus not be discussed.
832 *
833 * @code
834 *   template <int dim>
835 *   class Solver : public virtual Base<dim>
836 *   {
837 *   public:
838 *   Solver(Triangulation<dim> & triangulation,
839 *   const FiniteElement<dim> & fe,
840 *   const Quadrature<dim> & quadrature,
841 *   const Quadrature<dim - 1> &face_quadrature,
842 *   const Function<dim> & boundary_values);
843 *   virtual ~Solver() override;
844 *  
845 *   virtual void solve_problem() override;
846 *  
847 *   virtual void postprocess(
848 *   const Evaluation::EvaluationBase<dim> &postprocessor) const override;
849 *  
850 *   virtual unsigned int n_dofs() const override;
851 *  
852 *   protected:
854 *   const SmartPointer<const Quadrature<dim>> quadrature;
855 *   const SmartPointer<const Quadrature<dim - 1>> face_quadrature;
856 *   DoFHandler<dim> dof_handler;
857 *   Vector<double> solution;
858 *   const SmartPointer<const Function<dim>> boundary_values;
859 *  
860 *   virtual void assemble_rhs(Vector<double> &rhs) const = 0;
861 *  
862 *   private:
863 *   struct LinearSystem
864 *   {
865 *   LinearSystem(const DoFHandler<dim> &dof_handler);
866 *  
867 *   void solve(Vector<double> &solution) const;
868 *  
869 *   AffineConstraints<double> hanging_node_constraints;
870 *   SparsityPattern sparsity_pattern;
872 *   Vector<double> rhs;
873 *   };
874 *  
875 *  
876 * @endcode
877 *
878 * The remainder of the class is essentially a copy of @ref step_13 "step-13"
879 * as well, including the data structures and functions
880 * necessary to compute the linear system in parallel using the
881 * WorkStream framework:
882 *
883 * @code
884 *   struct AssemblyScratchData
885 *   {
886 *   AssemblyScratchData(const FiniteElement<dim> &fe,
887 *   const Quadrature<dim> & quadrature);
888 *   AssemblyScratchData(const AssemblyScratchData &scratch_data);
889 *  
890 *   FEValues<dim> fe_values;
891 *   };
892 *  
893 *   struct AssemblyCopyData
894 *   {
896 *   std::vector<types::global_dof_index> local_dof_indices;
897 *   };
898 *  
899 *  
900 *   void assemble_linear_system(LinearSystem &linear_system);
901 *  
902 *   void local_assemble_matrix(
903 *   const typename DoFHandler<dim>::active_cell_iterator &cell,
904 *   AssemblyScratchData & scratch_data,
905 *   AssemblyCopyData & copy_data) const;
906 *  
907 *  
908 *   void copy_local_to_global(const AssemblyCopyData &copy_data,
909 *   LinearSystem & linear_system) const;
910 *   };
911 *  
912 *  
913 *  
914 *   template <int dim>
915 *   Solver<dim>::Solver(Triangulation<dim> & triangulation,
916 *   const FiniteElement<dim> & fe,
917 *   const Quadrature<dim> & quadrature,
918 *   const Quadrature<dim - 1> &face_quadrature,
919 *   const Function<dim> & boundary_values)
920 *   : Base<dim>(triangulation)
921 *   , fe(&fe)
922 *   , quadrature(&quadrature)
923 *   , face_quadrature(&face_quadrature)
924 *   , dof_handler(triangulation)
925 *   , boundary_values(&boundary_values)
926 *   {}
927 *  
928 *  
929 *   template <int dim>
930 *   Solver<dim>::~Solver()
931 *   {
932 *   dof_handler.clear();
933 *   }
934 *  
935 *  
936 *   template <int dim>
937 *   void Solver<dim>::solve_problem()
938 *   {
939 *   dof_handler.distribute_dofs(*fe);
940 *   solution.reinit(dof_handler.n_dofs());
941 *  
942 *   LinearSystem linear_system(dof_handler);
943 *   assemble_linear_system(linear_system);
944 *   linear_system.solve(solution);
945 *   }
946 *  
947 *  
948 *   template <int dim>
949 *   void Solver<dim>::postprocess(
950 *   const Evaluation::EvaluationBase<dim> &postprocessor) const
951 *   {
952 *   postprocessor(dof_handler, solution);
953 *   }
954 *  
955 *  
956 *   template <int dim>
957 *   unsigned int Solver<dim>::n_dofs() const
958 *   {
959 *   return dof_handler.n_dofs();
960 *   }
961 *  
962 *  
963 * @endcode
964 *
965 * The following few functions and constructors are verbatim
966 * copies taken from @ref step_13 "step-13":
967 *
968 * @code
969 *   template <int dim>
970 *   void Solver<dim>::assemble_linear_system(LinearSystem &linear_system)
971 *   {
972 *   Threads::Task<void> rhs_task =
973 *   Threads::new_task(&Solver<dim>::assemble_rhs, *this, linear_system.rhs);
974 *  
975 *   auto worker =
976 *   [this](const typename DoFHandler<dim>::active_cell_iterator &cell,
977 *   AssemblyScratchData &scratch_data,
978 *   AssemblyCopyData & copy_data) {
979 *   this->local_assemble_matrix(cell, scratch_data, copy_data);
980 *   };
981 *  
982 *   auto copier = [this, &linear_system](const AssemblyCopyData &copy_data) {
983 *   this->copy_local_to_global(copy_data, linear_system);
984 *   };
985 *  
986 *   WorkStream::run(dof_handler.begin_active(),
987 *   dof_handler.end(),
988 *   worker,
989 *   copier,
990 *   AssemblyScratchData(*fe, *quadrature),
991 *   AssemblyCopyData());
992 *   linear_system.hanging_node_constraints.condense(linear_system.matrix);
993 *  
994 *   std::map<types::global_dof_index, double> boundary_value_map;
996 *   0,
997 *   *boundary_values,
998 *   boundary_value_map);
999 *  
1000 *   rhs_task.join();
1001 *   linear_system.hanging_node_constraints.condense(linear_system.rhs);
1002 *  
1003 *   MatrixTools::apply_boundary_values(boundary_value_map,
1004 *   linear_system.matrix,
1005 *   solution,
1006 *   linear_system.rhs);
1007 *   }
1008 *  
1009 *  
1010 *   template <int dim>
1011 *   Solver<dim>::AssemblyScratchData::AssemblyScratchData(
1012 *   const FiniteElement<dim> &fe,
1013 *   const Quadrature<dim> & quadrature)
1014 *   : fe_values(fe, quadrature, update_gradients | update_JxW_values)
1015 *   {}
1016 *  
1017 *  
1018 *   template <int dim>
1019 *   Solver<dim>::AssemblyScratchData::AssemblyScratchData(
1020 *   const AssemblyScratchData &scratch_data)
1021 *   : fe_values(scratch_data.fe_values.get_fe(),
1022 *   scratch_data.fe_values.get_quadrature(),
1024 *   {}
1025 *  
1026 *  
1027 *   template <int dim>
1028 *   void Solver<dim>::local_assemble_matrix(
1029 *   const typename DoFHandler<dim>::active_cell_iterator &cell,
1030 *   AssemblyScratchData & scratch_data,
1031 *   AssemblyCopyData & copy_data) const
1032 *   {
1033 *   const unsigned int dofs_per_cell = fe->n_dofs_per_cell();
1034 *   const unsigned int n_q_points = quadrature->size();
1035 *  
1036 *   copy_data.cell_matrix.reinit(dofs_per_cell, dofs_per_cell);
1037 *  
1038 *   copy_data.local_dof_indices.resize(dofs_per_cell);
1039 *  
1040 *   scratch_data.fe_values.reinit(cell);
1041 *  
1042 *   for (unsigned int q_point = 0; q_point < n_q_points; ++q_point)
1043 *   for (unsigned int i = 0; i < dofs_per_cell; ++i)
1044 *   for (unsigned int j = 0; j < dofs_per_cell; ++j)
1045 *   copy_data.cell_matrix(i, j) +=
1046 *   (scratch_data.fe_values.shape_grad(i, q_point) *
1047 *   scratch_data.fe_values.shape_grad(j, q_point) *
1048 *   scratch_data.fe_values.JxW(q_point));
1049 *  
1050 *   cell->get_dof_indices(copy_data.local_dof_indices);
1051 *   }
1052 *  
1053 *  
1054 *  
1055 *   template <int dim>
1056 *   void Solver<dim>::copy_local_to_global(const AssemblyCopyData &copy_data,
1057 *   LinearSystem &linear_system) const
1058 *   {
1059 *   for (unsigned int i = 0; i < copy_data.local_dof_indices.size(); ++i)
1060 *   for (unsigned int j = 0; j < copy_data.local_dof_indices.size(); ++j)
1061 *   linear_system.matrix.add(copy_data.local_dof_indices[i],
1062 *   copy_data.local_dof_indices[j],
1063 *   copy_data.cell_matrix(i, j));
1064 *   }
1065 *  
1066 *  
1067 * @endcode
1068 *
1069 * Now for the functions that implement actions in the linear
1070 * system class. First, the constructor initializes all data
1071 * elements to their correct sizes, and sets up a number of
1072 * additional data structures, such as constraints due to hanging
1073 * nodes. Since setting up the hanging nodes and finding out about
1074 * the nonzero elements of the matrix is independent, we do that
1075 * in parallel (if the library was configured to use concurrency,
1076 * at least; otherwise, the actions are performed
1077 * sequentially). Note that we start only one thread, and do the
1078 * second action in the main thread. Since only one thread is
1079 * generated, we don't use the <code>Threads::TaskGroup</code>
1080 * class here, but rather use the one created task object
1081 * directly to wait for this particular task's exit. The
1082 * approach is generally the same as the one we have used in
1083 * <code>Solver::assemble_linear_system()</code> above.
1084 *
1085
1086 *
1087 * Note that taking the address of the
1088 * <code>DoFTools::make_hanging_node_constraints</code> function
1089 * is a little tricky, since there are actually three functions of
1090 * this name, one for each supported space dimension. Taking
1091 * addresses of overloaded functions is somewhat complicated in
1092 * C++, since the address-of operator <code>&</code> in that case
1093 * returns a set of values (the addresses of all
1094 * functions with that name), and selecting the right one is then
1095 * the next step. If the context dictates which one to take (for
1096 * example by assigning to a function pointer of known type), then
1097 * the compiler can do that by itself, but if this set of pointers
1098 * shall be given as the argument to a function that takes a
1099 * template, the compiler could choose all without having a
1100 * preference for one. We therefore have to make it clear to the
1101 * compiler which one we would like to have; for this, we could
1102 * use a cast, but for more clarity, we assign it to a temporary
1103 * <code>mhnc_p</code> (short for <code>pointer to
1104 * make_hanging_node_constraints</code>) with the right type, and
1105 * using this pointer instead.
1106 *
1107 * @code
1108 *   template <int dim>
1109 *   Solver<dim>::LinearSystem::LinearSystem(const DoFHandler<dim> &dof_handler)
1110 *   {
1111 *   hanging_node_constraints.clear();
1112 *  
1113 *   void (*mhnc_p)(const DoFHandler<dim> &, AffineConstraints<double> &) =
1115 *  
1116 * @endcode
1117 *
1118 * Start a side task then continue on the main thread
1119 *
1120 * @code
1121 *   Threads::Task<void> side_task =
1122 *   Threads::new_task(mhnc_p, dof_handler, hanging_node_constraints);
1123 *  
1124 *   DynamicSparsityPattern dsp(dof_handler.n_dofs(), dof_handler.n_dofs());
1125 *   DoFTools::make_sparsity_pattern(dof_handler, dsp);
1126 *  
1127 *  
1128 *  
1129 * @endcode
1130 *
1131 * Wait for the side task to be done before going further
1132 *
1133 * @code
1134 *   side_task.join();
1135 *  
1136 *   hanging_node_constraints.close();
1137 *   hanging_node_constraints.condense(dsp);
1138 *   sparsity_pattern.copy_from(dsp);
1139 *  
1140 *   matrix.reinit(sparsity_pattern);
1141 *   rhs.reinit(dof_handler.n_dofs());
1142 *   }
1143 *  
1144 *  
1145 *  
1146 *   template <int dim>
1147 *   void Solver<dim>::LinearSystem::solve(Vector<double> &solution) const
1148 *   {
1149 *   SolverControl solver_control(5000, 1e-12);
1150 *   SolverCG<Vector<double>> cg(solver_control);
1151 *  
1152 *   PreconditionSSOR<SparseMatrix<double>> preconditioner;
1153 *   preconditioner.initialize(matrix, 1.2);
1154 *  
1155 *   cg.solve(matrix, solution, rhs, preconditioner);
1156 *  
1157 *   hanging_node_constraints.distribute(solution);
1158 *   }
1159 *  
1160 *  
1161 *  
1162 * @endcode
1163 *
1164 *
1165 * <a name="ThePrimalSolverclass"></a>
1166 * <h4>The PrimalSolver class</h4>
1167 *
1168
1169 *
1170 * The <code>PrimalSolver</code> class is also mostly unchanged except for
1171 * implementing the <code>output_solution</code> function. We keep the
1172 * <code>GlobalRefinement</code> and <code>RefinementKelly</code> classes
1173 * in this program, and they can then rely on the default implementation
1174 * of this function which simply outputs the primal solution. The class
1175 * implementing dual weighted error estimators will overload this function
1176 * itself, to also output the dual solution.
1177 *
1178 * @code
1179 *   template <int dim>
1180 *   class PrimalSolver : public Solver<dim>
1181 *   {
1182 *   public:
1183 *   PrimalSolver(Triangulation<dim> & triangulation,
1184 *   const FiniteElement<dim> & fe,
1185 *   const Quadrature<dim> & quadrature,
1186 *   const Quadrature<dim - 1> &face_quadrature,
1187 *   const Function<dim> & rhs_function,
1188 *   const Function<dim> & boundary_values);
1189 *  
1190 *   virtual void output_solution() const override;
1191 *  
1192 *   protected:
1193 *   const SmartPointer<const Function<dim>> rhs_function;
1194 *   virtual void assemble_rhs(Vector<double> &rhs) const override;
1195 *   };
1196 *  
1197 *  
1198 *   template <int dim>
1199 *   PrimalSolver<dim>::PrimalSolver(Triangulation<dim> & triangulation,
1200 *   const FiniteElement<dim> & fe,
1201 *   const Quadrature<dim> & quadrature,
1202 *   const Quadrature<dim - 1> &face_quadrature,
1203 *   const Function<dim> & rhs_function,
1204 *   const Function<dim> & boundary_values)
1205 *   : Base<dim>(triangulation)
1206 *   , Solver<dim>(triangulation,
1207 *   fe,
1208 *   quadrature,
1209 *   face_quadrature,
1210 *   boundary_values)
1211 *   , rhs_function(&rhs_function)
1212 *   {}
1213 *  
1214 *  
1215 *  
1216 *   template <int dim>
1217 *   void PrimalSolver<dim>::output_solution() const
1218 *   {
1219 *   DataOut<dim> data_out;
1220 *   data_out.attach_dof_handler(this->dof_handler);
1221 *   data_out.add_data_vector(this->solution, "solution");
1222 *   data_out.build_patches();
1223 *  
1224 *   std::ofstream out("solution-" + std::to_string(this->refinement_cycle) +
1225 *   ".vtu");
1226 *   data_out.write(out, DataOutBase::vtu);
1227 *   }
1228 *  
1229 *  
1230 *  
1231 *   template <int dim>
1232 *   void PrimalSolver<dim>::assemble_rhs(Vector<double> &rhs) const
1233 *   {
1234 *   FEValues<dim> fe_values(*this->fe,
1235 *   *this->quadrature,
1237 *   update_JxW_values);
1238 *  
1239 *   const unsigned int dofs_per_cell = this->fe->n_dofs_per_cell();
1240 *   const unsigned int n_q_points = this->quadrature->size();
1241 *  
1242 *   Vector<double> cell_rhs(dofs_per_cell);
1243 *   std::vector<double> rhs_values(n_q_points);
1244 *   std::vector<types::global_dof_index> local_dof_indices(dofs_per_cell);
1245 *  
1246 *   for (const auto &cell : this->dof_handler.active_cell_iterators())
1247 *   {
1248 *   cell_rhs = 0;
1249 *  
1250 *   fe_values.reinit(cell);
1251 *  
1252 *   rhs_function->value_list(fe_values.get_quadrature_points(),
1253 *   rhs_values);
1254 *  
1255 *   for (unsigned int q_point = 0; q_point < n_q_points; ++q_point)
1256 *   for (unsigned int i = 0; i < dofs_per_cell; ++i)
1257 *   cell_rhs(i) += (fe_values.shape_value(i, q_point) * // phi_i(x_q)
1258 *   rhs_values[q_point] * // f((x_q)
1259 *   fe_values.JxW(q_point)); // dx
1260 *  
1261 *   cell->get_dof_indices(local_dof_indices);
1262 *   for (unsigned int i = 0; i < dofs_per_cell; ++i)
1263 *   rhs(local_dof_indices[i]) += cell_rhs(i);
1264 *   }
1265 *   }
1266 *  
1267 *  
1268 * @endcode
1269 *
1270 *
1271 * <a name="TheRefinementGlobalandRefinementKellyclasses"></a>
1272 * <h4>The RefinementGlobal and RefinementKelly classes</h4>
1273 *
1274
1275 *
1276 * For the following two classes, the same applies as for most of the
1277 * above: the class is taken from the previous example as-is:
1278 *
1279 * @code
1280 *   template <int dim>
1281 *   class RefinementGlobal : public PrimalSolver<dim>
1282 *   {
1283 *   public:
1284 *   RefinementGlobal(Triangulation<dim> & coarse_grid,
1285 *   const FiniteElement<dim> & fe,
1286 *   const Quadrature<dim> & quadrature,
1287 *   const Quadrature<dim - 1> &face_quadrature,
1288 *   const Function<dim> & rhs_function,
1289 *   const Function<dim> & boundary_values);
1290 *  
1291 *   virtual void refine_grid() override;
1292 *   };
1293 *  
1294 *  
1295 *  
1296 *   template <int dim>
1297 *   RefinementGlobal<dim>::RefinementGlobal(
1298 *   Triangulation<dim> & coarse_grid,
1299 *   const FiniteElement<dim> & fe,
1300 *   const Quadrature<dim> & quadrature,
1301 *   const Quadrature<dim - 1> &face_quadrature,
1302 *   const Function<dim> & rhs_function,
1303 *   const Function<dim> & boundary_values)
1304 *   : Base<dim>(coarse_grid)
1305 *   , PrimalSolver<dim>(coarse_grid,
1306 *   fe,
1307 *   quadrature,
1308 *   face_quadrature,
1309 *   rhs_function,
1310 *   boundary_values)
1311 *   {}
1312 *  
1313 *  
1314 *  
1315 *   template <int dim>
1316 *   void RefinementGlobal<dim>::refine_grid()
1317 *   {
1318 *   this->triangulation->refine_global(1);
1319 *   }
1320 *  
1321 *  
1322 *  
1323 *   template <int dim>
1324 *   class RefinementKelly : public PrimalSolver<dim>
1325 *   {
1326 *   public:
1327 *   RefinementKelly(Triangulation<dim> & coarse_grid,
1328 *   const FiniteElement<dim> & fe,
1329 *   const Quadrature<dim> & quadrature,
1330 *   const Quadrature<dim - 1> &face_quadrature,
1331 *   const Function<dim> & rhs_function,
1332 *   const Function<dim> & boundary_values);
1333 *  
1334 *   virtual void refine_grid() override;
1335 *   };
1336 *  
1337 *  
1338 *  
1339 *   template <int dim>
1340 *   RefinementKelly<dim>::RefinementKelly(
1341 *   Triangulation<dim> & coarse_grid,
1342 *   const FiniteElement<dim> & fe,
1343 *   const Quadrature<dim> & quadrature,
1344 *   const Quadrature<dim - 1> &face_quadrature,
1345 *   const Function<dim> & rhs_function,
1346 *   const Function<dim> & boundary_values)
1347 *   : Base<dim>(coarse_grid)
1348 *   , PrimalSolver<dim>(coarse_grid,
1349 *   fe,
1350 *   quadrature,
1351 *   face_quadrature,
1352 *   rhs_function,
1353 *   boundary_values)
1354 *   {}
1355 *  
1356 *  
1357 *  
1358 *   template <int dim>
1359 *   void RefinementKelly<dim>::refine_grid()
1360 *   {
1361 *   Vector<float> estimated_error_per_cell(
1362 *   this->triangulation->n_active_cells());
1364 *   this->dof_handler,
1365 *   QGauss<dim - 1>(this->fe->degree + 1),
1366 *   std::map<types::boundary_id, const Function<dim> *>(),
1367 *   this->solution,
1368 *   estimated_error_per_cell);
1370 *   estimated_error_per_cell,
1371 *   0.3,
1372 *   0.03);
1373 *   this->triangulation->execute_coarsening_and_refinement();
1374 *   }
1375 *  
1376 *  
1377 *  
1378 * @endcode
1379 *
1380 *
1381 * <a name="TheRefinementWeightedKellyclass"></a>
1382 * <h4>The RefinementWeightedKelly class</h4>
1383 *
1384
1385 *
1386 * This class is a variant of the previous one, in that it allows to
1387 * weight the refinement indicators we get from the library's Kelly
1388 * indicator by some function. We include this class since the goal of
1389 * this example program is to demonstrate automatic refinement criteria
1390 * even for complex output quantities such as point values or stresses. If
1391 * we did not solve a dual problem and compute the weights thereof, we
1392 * would probably be tempted to give a hand-crafted weighting to the
1393 * indicators to account for the fact that we are going to evaluate these
1394 * quantities. This class accepts such a weighting function as argument to
1395 * its constructor:
1396 *
1397 * @code
1398 *   template <int dim>
1399 *   class RefinementWeightedKelly : public PrimalSolver<dim>
1400 *   {
1401 *   public:
1402 *   RefinementWeightedKelly(Triangulation<dim> & coarse_grid,
1403 *   const FiniteElement<dim> & fe,
1404 *   const Quadrature<dim> & quadrature,
1405 *   const Quadrature<dim - 1> &face_quadrature,
1406 *   const Function<dim> & rhs_function,
1407 *   const Function<dim> & boundary_values,
1408 *   const Function<dim> & weighting_function);
1409 *  
1410 *   virtual void refine_grid() override;
1411 *  
1412 *   private:
1413 *   const SmartPointer<const Function<dim>> weighting_function;
1414 *   };
1415 *  
1416 *  
1417 *  
1418 *   template <int dim>
1419 *   RefinementWeightedKelly<dim>::RefinementWeightedKelly(
1420 *   Triangulation<dim> & coarse_grid,
1421 *   const FiniteElement<dim> & fe,
1422 *   const Quadrature<dim> & quadrature,
1423 *   const Quadrature<dim - 1> &face_quadrature,
1424 *   const Function<dim> & rhs_function,
1425 *   const Function<dim> & boundary_values,
1426 *   const Function<dim> & weighting_function)
1427 *   : Base<dim>(coarse_grid)
1428 *   , PrimalSolver<dim>(coarse_grid,
1429 *   fe,
1430 *   quadrature,
1431 *   face_quadrature,
1432 *   rhs_function,
1433 *   boundary_values)
1434 *   , weighting_function(&weighting_function)
1435 *   {}
1436 *  
1437 *  
1438 *  
1439 * @endcode
1440 *
1441 * Now, here comes the main function, including the weighting:
1442 *
1443 * @code
1444 *   template <int dim>
1445 *   void RefinementWeightedKelly<dim>::refine_grid()
1446 *   {
1447 * @endcode
1448 *
1449 * First compute some residual based error indicators for all cells by a
1450 * method already implemented in the library. What exactly we compute
1451 * here is described in more detail in the documentation of that class.
1452 *
1453 * @code
1454 *   Vector<float> estimated_error_per_cell(
1455 *   this->triangulation->n_active_cells());
1456 *   std::map<types::boundary_id, const Function<dim> *> dummy_function_map;
1457 *   KellyErrorEstimator<dim>::estimate(this->dof_handler,
1458 *   *this->face_quadrature,
1459 *   dummy_function_map,
1460 *   this->solution,
1461 *   estimated_error_per_cell);
1462 *  
1463 * @endcode
1464 *
1465 * Next weigh each entry in the vector of indicators by the value of the
1466 * function given to the constructor, evaluated at the cell center. We
1467 * need to write the result into the vector entry that corresponds to the
1468 * current cell, which we can obtain by asking the cell what its index
1469 * among all active cells is using CellAccessor::active_cell_index(). (In
1470 * reality, this index is zero for the first cell we handle in the loop,
1471 * one for the second cell, etc., and we could as well just keep track of
1472 * this index using an integer counter; but using
1473 * CellAccessor::active_cell_index() makes this more explicit.)
1474 *
1475 * @code
1476 *   for (const auto &cell : this->dof_handler.active_cell_iterators())
1477 *   estimated_error_per_cell(cell->active_cell_index()) *=
1478 *   weighting_function->value(cell->center());
1479 *  
1480 *   GridRefinement::refine_and_coarsen_fixed_number(*this->triangulation,
1481 *   estimated_error_per_cell,
1482 *   0.3,
1483 *   0.03);
1484 *   this->triangulation->execute_coarsening_and_refinement();
1485 *   }
1486 *  
1487 *   } // namespace LaplaceSolver
1488 *  
1489 *  
1490 * @endcode
1491 *
1492 *
1493 * <a name="Equationdata"></a>
1494 * <h3>Equation data</h3>
1495 *
1496
1497 *
1498 * In this example program, we work with the same data sets as in the
1499 * previous one, but as it may so happen that someone wants to run the
1500 * program with different boundary values and right hand side functions, or
1501 * on a different grid, we show a simple technique to do exactly that. For
1502 * more clarity, we furthermore pack everything that has to do with equation
1503 * data into a namespace of its own.
1504 *
1505
1506 *
1507 * The underlying assumption is that this is a research program, and that
1508 * there we often have a number of test cases that consist of a domain, a
1509 * right hand side, boundary values, possibly a specified coefficient, and a
1510 * number of other parameters. They often vary all at the same time when
1511 * shifting from one example to another. To make handling such sets of
1512 * problem description parameters simple is the goal of the following.
1513 *
1514
1515 *
1516 * Basically, the idea is this: let us have a structure for each set of
1517 * data, in which we pack everything that describes a test case: here, these
1518 * are two subclasses, one called <code>BoundaryValues</code> for the
1519 * boundary values of the exact solution, and one called
1520 * <code>RightHandSide</code>, and then a way to generate the coarse
1521 * grid. Since the solution of the previous example program looked like
1522 * curved ridges, we use this name here for the enclosing class. Note that
1523 * the names of the two inner classes have to be the same for all enclosing
1524 * test case classes, and also that we have attached the dimension template
1525 * argument to the enclosing class rather than to the inner ones, to make
1526 * further processing simpler. (From a language viewpoint, a namespace
1527 * would be better to encapsulate these inner classes, rather than a
1528 * structure. However, namespaces cannot be given as template arguments, so
1529 * we use a structure to allow a second object to select from within its
1530 * given argument. The enclosing structure, of course, has no member
1531 * variables apart from the classes it declares, and a static function to
1532 * generate the coarse mesh; it will in general never be instantiated.)
1533 *
1534
1535 *
1536 * The idea is then the following (this is the right time to also take a
1537 * brief look at the code below): we can generate objects for boundary
1538 * values and right hand side by simply giving the name of the outer class
1539 * as a template argument to a class which we call here
1540 * <code>Data::SetUp</code>, and it then creates objects for the inner
1541 * classes. In this case, to get all that characterizes the curved ridge
1542 * solution, we would simply generate an instance of
1543 * <code>Data::SetUp@<Data::CurvedRidge@></code>, and everything we need to
1544 * know about the solution would be static member variables and functions of
1545 * that object.
1546 *
1547
1548 *
1549 * This approach might seem like overkill in this case, but will become very
1550 * handy once a certain set up is not only characterized by Dirichlet
1551 * boundary values and a right hand side function, but in addition by
1552 * material properties, Neumann values, different boundary descriptors,
1553 * etc. In that case, the <code>SetUp</code> class might consist of a dozen
1554 * or more objects, and each descriptor class (like the
1555 * <code>CurvedRidges</code> class below) would have to provide them. Then,
1556 * you will be happy to be able to change from one set of data to another by
1557 * only changing the template argument to the <code>SetUp</code> class at
1558 * one place, rather than at many.
1559 *
1560
1561 *
1562 * With this framework for different test cases, we are almost finished, but
1563 * one thing remains: by now we can select statically, by changing one
1564 * template argument, which data set to choose. In order to be able to do
1565 * that dynamically, i.e. at run time, we need a base class. This we provide
1566 * in the obvious way, see below, with virtual abstract functions. It forces
1567 * us to introduce a second template parameter <code>dim</code> which we
1568 * need for the base class (which could be avoided using some template
1569 * magic, but we omit that), but that's all.
1570 *
1571
1572 *
1573 * Adding new testcases is now simple, you don't have to touch the framework
1574 * classes, only a structure like the <code>CurvedRidges</code> one is
1575 * needed.
1576 *
1577 * @code
1578 *   namespace Data
1579 *   {
1580 * @endcode
1581 *
1582 *
1583 * <a name="TheSetUpBaseandSetUpclasses"></a>
1584 * <h4>The SetUpBase and SetUp classes</h4>
1585 *
1586
1587 *
1588 * Based on the above description, the <code>SetUpBase</code> class then
1589 * looks as follows. To allow using the <code>SmartPointer</code> class
1590 * with this class, we derived from the <code>Subscriptor</code> class.
1591 *
1592 * @code
1593 *   template <int dim>
1594 *   struct SetUpBase : public Subscriptor
1595 *   {
1596 *   virtual const Function<dim> &get_boundary_values() const = 0;
1597 *  
1598 *   virtual const Function<dim> &get_right_hand_side() const = 0;
1599 *  
1600 *   virtual void
1601 *   create_coarse_grid(Triangulation<dim> &coarse_grid) const = 0;
1602 *   };
1603 *  
1604 *  
1605 * @endcode
1606 *
1607 * And now for the derived class that takes the template argument as
1608 * explained above.
1609 *
1610
1611 *
1612 * Here we pack the data elements into private variables, and allow access
1613 * to them through the methods of the base class.
1614 *
1615 * @code
1616 *   template <class Traits, int dim>
1617 *   struct SetUp : public SetUpBase<dim>
1618 *   {
1619 *   virtual const Function<dim> &get_boundary_values() const override;
1620 *  
1621 *   virtual const Function<dim> &get_right_hand_side() const override;
1622 *  
1623 *  
1624 *   virtual void
1625 *   create_coarse_grid(Triangulation<dim> &coarse_grid) const override;
1626 *  
1627 *   private:
1628 *   static const typename Traits::BoundaryValues boundary_values;
1629 *   static const typename Traits::RightHandSide right_hand_side;
1630 *   };
1631 *  
1632 * @endcode
1633 *
1634 * We have to provide definitions for the static member variables of the
1635 * above class:
1636 *
1637 * @code
1638 *   template <class Traits, int dim>
1639 *   const typename Traits::BoundaryValues SetUp<Traits, dim>::boundary_values;
1640 *   template <class Traits, int dim>
1641 *   const typename Traits::RightHandSide SetUp<Traits, dim>::right_hand_side;
1642 *  
1643 * @endcode
1644 *
1645 * And definitions of the member functions:
1646 *
1647 * @code
1648 *   template <class Traits, int dim>
1649 *   const Function<dim> &SetUp<Traits, dim>::get_boundary_values() const
1650 *   {
1651 *   return boundary_values;
1652 *   }
1653 *  
1654 *  
1655 *   template <class Traits, int dim>
1656 *   const Function<dim> &SetUp<Traits, dim>::get_right_hand_side() const
1657 *   {
1658 *   return right_hand_side;
1659 *   }
1660 *  
1661 *  
1662 *   template <class Traits, int dim>
1663 *   void SetUp<Traits, dim>::create_coarse_grid(
1664 *   Triangulation<dim> &coarse_grid) const
1665 *   {
1666 *   Traits::create_coarse_grid(coarse_grid);
1667 *   }
1668 *  
1669 *  
1670 * @endcode
1671 *
1672 *
1673 * <a name="TheCurvedRidgesclass"></a>
1674 * <h4>The CurvedRidges class</h4>
1675 *
1676
1677 *
1678 * The class that is used to describe the boundary values and right hand
1679 * side of the <code>curved ridge</code> problem already used in the
1680 * @ref step_13 "step-13" example program is then like so:
1681 *
1682 * @code
1683 *   template <int dim>
1684 *   struct CurvedRidges
1685 *   {
1686 *   class BoundaryValues : public Function<dim>
1687 *   {
1688 *   public:
1689 *   virtual double value(const Point<dim> & p,
1690 *   const unsigned int component) const;
1691 *   };
1692 *  
1693 *  
1694 *   class RightHandSide : public Function<dim>
1695 *   {
1696 *   public:
1697 *   virtual double value(const Point<dim> & p,
1698 *   const unsigned int component) const;
1699 *   };
1700 *  
1701 *   static void create_coarse_grid(Triangulation<dim> &coarse_grid);
1702 *   };
1703 *  
1704 *  
1705 *   template <int dim>
1706 *   double CurvedRidges<dim>::BoundaryValues::value(
1707 *   const Point<dim> &p,
1708 *   const unsigned int /*component*/) const
1709 *   {
1710 *   double q = p(0);
1711 *   for (unsigned int i = 1; i < dim; ++i)
1712 *   q += std::sin(10 * p(i) + 5 * p(0) * p(0));
1713 *   const double exponential = std::exp(q);
1714 *   return exponential;
1715 *   }
1716 *  
1717 *  
1718 *  
1719 *   template <int dim>
1720 *   double CurvedRidges<dim>::RightHandSide::value(
1721 *   const Point<dim> &p,
1722 *   const unsigned int /*component*/) const
1723 *   {
1724 *   double q = p(0);
1725 *   for (unsigned int i = 1; i < dim; ++i)
1726 *   q += std::sin(10 * p(i) + 5 * p(0) * p(0));
1727 *   const double u = std::exp(q);
1728 *   double t1 = 1, t2 = 0, t3 = 0;
1729 *   for (unsigned int i = 1; i < dim; ++i)
1730 *   {
1731 *   t1 += std::cos(10 * p(i) + 5 * p(0) * p(0)) * 10 * p(0);
1732 *   t2 += 10 * std::cos(10 * p(i) + 5 * p(0) * p(0)) -
1733 *   100 * std::sin(10 * p(i) + 5 * p(0) * p(0)) * p(0) * p(0);
1734 *   t3 += 100 * std::cos(10 * p(i) + 5 * p(0) * p(0)) *
1735 *   std::cos(10 * p(i) + 5 * p(0) * p(0)) -
1736 *   100 * std::sin(10 * p(i) + 5 * p(0) * p(0));
1737 *   }
1738 *   t1 = t1 * t1;
1739 *  
1740 *   return -u * (t1 + t2 + t3);
1741 *   }
1742 *  
1743 *  
1744 *   template <int dim>
1745 *   void CurvedRidges<dim>::create_coarse_grid(Triangulation<dim> &coarse_grid)
1746 *   {
1747 *   GridGenerator::hyper_cube(coarse_grid, -1, 1);
1748 *   coarse_grid.refine_global(2);
1749 *   }
1750 *  
1751 *  
1752 * @endcode
1753 *
1754 *
1755 * <a name="TheExercise_2_3class"></a>
1756 * <h4>The Exercise_2_3 class</h4>
1757 *
1758
1759 *
1760 * This example program was written while giving practical courses for a
1761 * lecture on adaptive finite element methods and duality based error
1762 * estimates. For these courses, we had one exercise, which required to
1763 * solve the Laplace equation with constant right hand side on a square
1764 * domain with a square hole in the center, and zero boundary
1765 * values. Since the implementation of the properties of this problem is
1766 * so particularly simple here, lets do it. As the number of the exercise
1767 * was 2.3, we take the liberty to retain this name for the class as well.
1768 *
1769 * @code
1770 *   template <int dim>
1771 *   struct Exercise_2_3
1772 *   {
1773 * @endcode
1774 *
1775 * We need a class to denote the boundary values of the problem. In this
1776 * case, this is simple: it's the zero function, so don't even declare a
1777 * class, just an alias:
1778 *
1779 * @code
1780 *   using BoundaryValues = Functions::ZeroFunction<dim>;
1781 *  
1782 * @endcode
1783 *
1784 * Second, a class that denotes the right hand side. Since they are
1785 * constant, just subclass the corresponding class of the library and be
1786 * done:
1787 *
1788 * @code
1789 *   class RightHandSide : public Functions::ConstantFunction<dim>
1790 *   {
1791 *   public:
1792 *   RightHandSide()
1793 *   : Functions::ConstantFunction<dim>(1.)
1794 *   {}
1795 *   };
1796 *  
1797 * @endcode
1798 *
1799 * Finally a function to generate the coarse grid. This is somewhat more
1800 * complicated here, see immediately below.
1801 *
1802 * @code
1803 *   static void create_coarse_grid(Triangulation<dim> &coarse_grid);
1804 *   };
1805 *  
1806 *  
1807 * @endcode
1808 *
1809 * As stated above, the grid for this example is the square [-1,1]^2 with
1810 * the square [-1/2,1/2]^2 as hole in it. We create the coarse grid as 4
1811 * times 4 cells with the middle four ones missing. To understand how
1812 * exactly the mesh is going to look, it may be simplest to just look
1813 * at the "Results" section of this tutorial program first. In general,
1814 * if you'd like to understand more about creating meshes either from
1815 * scratch by hand, as we do here, or using other techniques, you
1816 * should take a look at @ref step_49 "step-49".
1817 *
1818
1819 *
1820 * Of course, the example has an extension to 3d, but since this function
1821 * cannot be written in a dimension independent way we choose not to
1822 * implement this here, but rather only specialize the template for
1823 * dim=2. If you compile the program for 3d, you'll get a message from the
1824 * linker that this function is not implemented for 3d, and needs to be
1825 * provided.
1826 *
1827
1828 *
1829 * For the creation of this geometry, the library has no predefined
1830 * method. In this case, the geometry is still simple enough to do the
1831 * creation by hand, rather than using a mesh generator.
1832 *
1833 * @code
1834 *   template <>
1835 *   void Exercise_2_3<2>::create_coarse_grid(Triangulation<2> &coarse_grid)
1836 *   {
1837 * @endcode
1838 *
1839 * We first define the space dimension, to allow those parts of the
1840 * function that are actually dimension independent to use this
1841 * variable. That makes it simpler if you later take this as a starting
1842 * point to implement a 3d version of this mesh. The next step is then
1843 * to have a list of vertices. Here, they are 24 (5 times 5, with the
1844 * middle one omitted). It is probably best to draw a sketch here.
1845 *
1846 * @code
1847 *   const unsigned int dim = 2;
1848 *  
1849 *   const std::vector<Point<2>> vertices = {
1850 *   {-1.0, -1.0}, {-0.5, -1.0}, {+0.0, -1.0}, {+0.5, -1.0}, {+1.0, -1.0},
1851 *   {-1.0, -0.5}, {-0.5, -0.5}, {+0.0, -0.5}, {+0.5, -0.5}, {+1.0, -0.5},
1852 *   {-1.0, +0.0}, {-0.5, +0.0}, {+0.5, +0.0}, {+1.0, +0.0},
1853 *   {-1.0, +0.5}, {-0.5, +0.5}, {+0.0, +0.5}, {+0.5, +0.5}, {+1.0, +0.5},
1854 *   {-1.0, +1.0}, {-0.5, +1.0}, {+0.0, +1.0}, {+0.5, +1.0}, {+1.0, +1.0}};
1855 *  
1856 * @endcode
1857 *
1858 * Next, we have to define the cells and the vertices they contain.
1859 *
1860 * @code
1861 *   const std::vector<std::array<int, GeometryInfo<dim>::vertices_per_cell>>
1862 *   cell_vertices = {{{0, 1, 5, 6}},
1863 *   {{1, 2, 6, 7}},
1864 *   {{2, 3, 7, 8}},
1865 *   {{3, 4, 8, 9}},
1866 *   {{5, 6, 10, 11}},
1867 *   {{8, 9, 12, 13}},
1868 *   {{10, 11, 14, 15}},
1869 *   {{12, 13, 17, 18}},
1870 *   {{14, 15, 19, 20}},
1871 *   {{15, 16, 20, 21}},
1872 *   {{16, 17, 21, 22}},
1873 *   {{17, 18, 22, 23}}};
1874 *  
1875 *   const unsigned int n_cells = cell_vertices.size();
1876 *  
1877 * @endcode
1878 *
1879 * Again, we generate a C++ vector type from this, but this time by
1880 * looping over the cells (yes, this is boring). Additionally, we set
1881 * the material indicator to zero for all the cells:
1882 *
1883 * @code
1884 *   std::vector<CellData<dim>> cells(n_cells, CellData<dim>());
1885 *   for (unsigned int i = 0; i < n_cells; ++i)
1886 *   {
1887 *   for (unsigned int j = 0; j < cell_vertices[i].size(); ++j)
1888 *   cells[i].vertices[j] = cell_vertices[i][j];
1889 *   cells[i].material_id = 0;
1890 *   }
1891 *  
1892 * @endcode
1893 *
1894 * Finally pass all this information to the library to generate a
1895 * triangulation. The last parameter may be used to pass information
1896 * about non-zero boundary indicators at certain faces of the
1897 * triangulation to the library, but we don't want that here, so we give
1898 * an empty object:
1899 *
1900 * @code
1901 *   coarse_grid.create_triangulation(vertices, cells, SubCellData());
1902 *  
1903 * @endcode
1904 *
1905 * And since we want that the evaluation point (3/4,3/4) in this example
1906 * is a grid point, we refine once globally:
1907 *
1908 * @code
1909 *   coarse_grid.refine_global(1);
1910 *   }
1911 *   } // namespace Data
1912 *  
1913 * @endcode
1914 *
1915 *
1916 * <a name="Discussion"></a>
1917 * <h4>Discussion</h4>
1918 *
1919
1920 *
1921 * As you have now read through this framework, you may be wondering why we
1922 * have not chosen to implement the classes implementing a certain setup
1923 * (like the <code>CurvedRidges</code> class) directly as classes derived
1924 * from <code>Data::SetUpBase</code>. Indeed, we could have done very well
1925 * so. The only reason is that then we would have to have member variables
1926 * for the solution and right hand side classes in the
1927 * <code>CurvedRidges</code> class, as well as member functions overloading
1928 * the abstract functions of the base class giving access to these member
1929 * variables. The <code>SetUp</code> class has the sole reason to relieve us
1930 * from the need to reiterate these member variables and functions that
1931 * would be necessary in all such classes. In some way, the template
1932 * mechanism here only provides a way to have default implementations for a
1933 * number of functions that depend on external quantities and can thus not
1934 * be provided using normal virtual functions, at least not without the help
1935 * of templates.
1936 *
1937
1938 *
1939 * However, there might be good reasons to actually implement classes
1940 * derived from <code>Data::SetUpBase</code>, for example if the solution or
1941 * right hand side classes require constructors that take arguments, which
1942 * the <code>Data::SetUpBase</code> class cannot provide. In that case,
1943 * subclassing is a worthwhile strategy. Other possibilities for special
1944 * cases are to derive from <code>Data::SetUp@<SomeSetUp@></code> where
1945 * <code>SomeSetUp</code> denotes a class, or even to explicitly specialize
1946 * <code>Data::SetUp@<SomeSetUp@></code>. The latter allows to transparently
1947 * use the way the <code>SetUp</code> class is used for other set-ups, but
1948 * with special actions taken for special arguments.
1949 *
1950
1951 *
1952 * A final observation favoring the approach taken here is the following: we
1953 * have found numerous times that when starting a project, the number of
1954 * parameters (usually boundary values, right hand side, coarse grid, just
1955 * as here) was small, and the number of test cases was small as well. One
1956 * then starts out by handcoding them into a number of <code>switch</code>
1957 * statements. Over time, projects grow, and so does the number of test
1958 * cases. The number of <code>switch</code> statements grows with that, and
1959 * their length as well, and one starts to find ways to consider impossible
1960 * examples where domains, boundary values, and right hand sides do not fit
1961 * together any more, and starts losing the overview over the whole
1962 * structure. Encapsulating everything belonging to a certain test case into
1963 * a structure of its own has proven worthwhile for this, as it keeps
1964 * everything that belongs to one test case in one place. Furthermore, it
1965 * allows to put these things all in one or more files that are only devoted
1966 * to test cases and their data, without having to bring their actual
1967 * implementation into contact with the rest of the program.
1968 *
1969
1970 *
1971 *
1972
1973 *
1974 *
1975 * <a name="Dualfunctionals"></a>
1976 * <h3>Dual functionals</h3>
1977 *
1978
1979 *
1980 * As with the other components of the program, we put everything we need to
1981 * describe dual functionals into a namespace of its own, and define an
1982 * abstract base class that provides the interface the class solving the
1983 * dual problem needs for its work.
1984 *
1985
1986 *
1987 * We will then implement two such classes, for the evaluation of a point
1988 * value and of the derivative of the solution at that point. For these
1989 * functionals we already have the corresponding evaluation objects, so they
1990 * are complementary.
1991 *
1992 * @code
1993 *   namespace DualFunctional
1994 *   {
1995 * @endcode
1996 *
1997 *
1998 * <a name="TheDualFunctionalBaseclass"></a>
1999 * <h4>The DualFunctionalBase class</h4>
2000 *
2001
2002 *
2003 * First start with the base class for dual functionals. Since for linear
2004 * problems the characteristics of the dual problem play a role only in
2005 * the right hand side, we only need to provide for a function that
2006 * assembles the right hand side for a given discretization:
2007 *
2008 * @code
2009 *   template <int dim>
2010 *   class DualFunctionalBase : public Subscriptor
2011 *   {
2012 *   public:
2013 *   virtual void assemble_rhs(const DoFHandler<dim> &dof_handler,
2014 *   Vector<double> & rhs) const = 0;
2015 *   };
2016 *  
2017 *  
2018 * @endcode
2019 *
2020 *
2021 * <a name="ThedualfunctionalPointValueEvaluationclass"></a>
2022 * <h4>The dual functional PointValueEvaluation class</h4>
2023 *
2024
2025 *
2026 * As a first application, we consider the functional corresponding to the
2027 * evaluation of the solution's value at a given point which again we
2028 * assume to be a vertex. Apart from the constructor that takes and stores
2029 * the evaluation point, this class consists only of the function that
2030 * implements assembling the right hand side.
2031 *
2032 * @code
2033 *   template <int dim>
2034 *   class PointValueEvaluation : public DualFunctionalBase<dim>
2035 *   {
2036 *   public:
2037 *   PointValueEvaluation(const Point<dim> &evaluation_point);
2038 *  
2039 *   virtual void assemble_rhs(const DoFHandler<dim> &dof_handler,
2040 *   Vector<double> & rhs) const override;
2041 *  
2042 *   DeclException1(
2043 *   ExcEvaluationPointNotFound,
2044 *   Point<dim>,
2045 *   << "The evaluation point " << arg1
2046 *   << " was not found among the vertices of the present grid.");
2047 *  
2048 *   protected:
2049 *   const Point<dim> evaluation_point;
2050 *   };
2051 *  
2052 *  
2053 *   template <int dim>
2054 *   PointValueEvaluation<dim>::PointValueEvaluation(
2055 *   const Point<dim> &evaluation_point)
2056 *   : evaluation_point(evaluation_point)
2057 *   {}
2058 *  
2059 *  
2060 * @endcode
2061 *
2062 * As for doing the main purpose of the class, assembling the right hand
2063 * side, let us first consider what is necessary: The right hand side of
2064 * the dual problem is a vector of values J(phi_i), where J is the error
2065 * functional, and phi_i is the i-th shape function. Here, J is the
2066 * evaluation at the point x0, i.e. J(phi_i)=phi_i(x0).
2067 *
2068
2069 *
2070 * Now, we have assumed that the evaluation point is a vertex. Thus, for
2071 * the usual finite elements we might be using in this program, we can
2072 * take for granted that at such a point exactly one shape function is
2073 * nonzero, and in particular has the value one. Thus, we set the right
2074 * hand side vector to all-zeros, then seek for the shape function
2075 * associated with that point and set the corresponding value of the right
2076 * hand side vector to one:
2077 *
2078 * @code
2079 *   template <int dim>
2080 *   void
2081 *   PointValueEvaluation<dim>::assemble_rhs(const DoFHandler<dim> &dof_handler,
2082 *   Vector<double> & rhs) const
2083 *   {
2084 * @endcode
2085 *
2086 * So, first set everything to zeros...
2087 *
2088 * @code
2089 *   rhs.reinit(dof_handler.n_dofs());
2090 *  
2091 * @endcode
2092 *
2093 * ...then loop over cells and find the evaluation point among the
2094 * vertices (or very close to a vertex, which may happen due to floating
2095 * point round-off):
2096 *
2097 * @code
2098 *   for (const auto &cell : dof_handler.active_cell_iterators())
2099 *   for (const auto vertex : cell->vertex_indices())
2100 *   if (cell->vertex(vertex).distance(evaluation_point) <
2101 *   cell->diameter() * 1e-8)
2102 *   {
2103 * @endcode
2104 *
2105 * Ok, found, so set corresponding entry, and leave function
2106 * since we are finished:
2107 *
2108 * @code
2109 *   rhs(cell->vertex_dof_index(vertex, 0)) = 1;
2110 *   return;
2111 *   }
2112 *  
2113 * @endcode
2114 *
2115 * Finally, a sanity check: if we somehow got here, then we must have
2116 * missed the evaluation point, so raise an exception unconditionally:
2117 *
2118 * @code
2119 *   AssertThrow(false, ExcEvaluationPointNotFound(evaluation_point));
2120 *   }
2121 *  
2122 *  
2123 * @endcode
2124 *
2125 *
2126 * <a name="ThedualfunctionalPointXDerivativeEvaluationclass"></a>
2127 * <h4>The dual functional PointXDerivativeEvaluation class</h4>
2128 *
2129
2130 *
2131 * As second application, we again consider the evaluation of the
2132 * x-derivative of the solution at one point. Again, the declaration of
2133 * the class, and the implementation of its constructor is not too
2134 * interesting:
2135 *
2136 * @code
2137 *   template <int dim>
2138 *   class PointXDerivativeEvaluation : public DualFunctionalBase<dim>
2139 *   {
2140 *   public:
2141 *   PointXDerivativeEvaluation(const Point<dim> &evaluation_point);
2142 *  
2143 *   virtual void assemble_rhs(const DoFHandler<dim> &dof_handler,
2144 *   Vector<double> & rhs) const;
2145 *  
2146 *   DeclException1(
2147 *   ExcEvaluationPointNotFound,
2148 *   Point<dim>,
2149 *   << "The evaluation point " << arg1
2150 *   << " was not found among the vertices of the present grid.");
2151 *  
2152 *   protected:
2153 *   const Point<dim> evaluation_point;
2154 *   };
2155 *  
2156 *  
2157 *   template <int dim>
2158 *   PointXDerivativeEvaluation<dim>::PointXDerivativeEvaluation(
2159 *   const Point<dim> &evaluation_point)
2160 *   : evaluation_point(evaluation_point)
2161 *   {}
2162 *  
2163 *  
2164 * @endcode
2165 *
2166 * What is interesting is the implementation of this functional: here,
2167 * J(phi_i)=d/dx phi_i(x0).
2168 *
2169
2170 *
2171 * We could, as in the implementation of the respective evaluation object
2172 * take the average of the gradients of each shape function phi_i at this
2173 * evaluation point. However, we take a slightly different approach: we
2174 * simply take the average over all cells that surround this point. The
2175 * question which cells <code>surrounds</code> the evaluation point is
2176 * made dependent on the mesh width by including those cells for which the
2177 * distance of the cell's midpoint to the evaluation point is less than
2178 * the cell's diameter.
2179 *
2180
2181 *
2182 * Taking the average of the gradient over the area/volume of these cells
2183 * leads to a dual solution which is very close to the one which would
2184 * result from the point evaluation of the gradient. It is simple to
2185 * justify theoretically that this does not change the method
2186 * significantly.
2187 *
2188 * @code
2189 *   template <int dim>
2190 *   void PointXDerivativeEvaluation<dim>::assemble_rhs(
2191 *   const DoFHandler<dim> &dof_handler,
2192 *   Vector<double> & rhs) const
2193 *   {
2194 * @endcode
2195 *
2196 * Again, first set all entries to zero:
2197 *
2198 * @code
2199 *   rhs.reinit(dof_handler.n_dofs());
2200 *  
2201 * @endcode
2202 *
2203 * Initialize a <code>FEValues</code> object with a quadrature formula,
2204 * have abbreviations for the number of quadrature points and shape
2205 * functions...
2206 *
2207 * @code
2208 *   QGauss<dim> quadrature(dof_handler.get_fe().degree + 1);
2209 *   FEValues<dim> fe_values(dof_handler.get_fe(),
2210 *   quadrature,
2211 *   update_gradients | update_quadrature_points |
2212 *   update_JxW_values);
2213 *   const unsigned int n_q_points = fe_values.n_quadrature_points;
2214 *   const unsigned int dofs_per_cell = dof_handler.get_fe().dofs_per_cell;
2215 *  
2216 * @endcode
2217 *
2218 * ...and have two objects that are used to store the global indices of
2219 * the degrees of freedom on a cell, and the values of the gradients of
2220 * the shape functions at the quadrature points:
2221 *
2222 * @code
2223 *   Vector<double> cell_rhs(dofs_per_cell);
2224 *   std::vector<unsigned int> local_dof_indices(dofs_per_cell);
2225 *  
2226 * @endcode
2227 *
2228 * Finally have a variable in which we will sum up the area/volume of
2229 * the cells over which we integrate, by integrating the unit functions
2230 * on these cells:
2231 *
2232 * @code
2233 *   double total_volume = 0;
2234 *  
2235 * @endcode
2236 *
2237 * Then start the loop over all cells, and select those cells which are
2238 * close enough to the evaluation point:
2239 *
2240 * @code
2241 *   for (const auto &cell : dof_handler.active_cell_iterators())
2242 *   if (cell->center().distance(evaluation_point) <= cell->diameter())
2243 *   {
2244 * @endcode
2245 *
2246 * If we have found such a cell, then initialize the
2247 * <code>FEValues</code> object and integrate the x-component of
2248 * the gradient of each shape function, as well as the unit
2249 * function for the total area/volume.
2250 *
2251 * @code
2252 *   fe_values.reinit(cell);
2253 *   cell_rhs = 0;
2254 *  
2255 *   for (unsigned int q = 0; q < n_q_points; ++q)
2256 *   {
2257 *   for (unsigned int i = 0; i < dofs_per_cell; ++i)
2258 *   cell_rhs(i) +=
2259 *   fe_values.shape_grad(i, q)[0] // (d/dx phi_i(x_q))
2260 *   * fe_values.JxW(q); // * dx
2261 *   total_volume += fe_values.JxW(q);
2262 *   }
2263 *  
2264 * @endcode
2265 *
2266 * If we have the local contributions, distribute them to the
2267 * global vector:
2268 *
2269 * @code
2270 *   cell->get_dof_indices(local_dof_indices);
2271 *   for (unsigned int i = 0; i < dofs_per_cell; ++i)
2272 *   rhs(local_dof_indices[i]) += cell_rhs(i);
2273 *   }
2274 *  
2275 * @endcode
2276 *
2277 * After we have looped over all cells, check whether we have found any
2278 * at all, by making sure that their volume is non-zero. If not, then
2279 * the results will be botched, as the right hand side should then still
2280 * be zero, so throw an exception:
2281 *
2282 * @code
2283 *   AssertThrow(total_volume > 0,
2284 *   ExcEvaluationPointNotFound(evaluation_point));
2285 *  
2286 * @endcode
2287 *
2288 * Finally, we have by now only integrated the gradients of the shape
2289 * functions, not taking their mean value. We fix this by dividing by
2290 * the measure of the volume over which we have integrated:
2291 *
2292 * @code
2293 *   rhs /= total_volume;
2294 *   }
2295 *  
2296 *  
2297 *   } // namespace DualFunctional
2298 *  
2299 *  
2300 * @endcode
2301 *
2302 *
2303 * <a name="ExtendingtheLaplaceSolvernamespace"></a>
2304 * <h3>Extending the LaplaceSolver namespace</h3>
2305 *
2306 * @code
2307 *   namespace LaplaceSolver
2308 *   {
2309 * @endcode
2310 *
2311 *
2312 * <a name="TheDualSolverclass"></a>
2313 * <h4>The DualSolver class</h4>
2314 *
2315
2316 *
2317 * In the same way as the <code>PrimalSolver</code> class above, we now
2318 * implement a <code>DualSolver</code>. It has all the same features, the
2319 * only difference is that it does not take a function object denoting a
2320 * right hand side object, but now takes a <code>DualFunctionalBase</code>
2321 * object that will assemble the right hand side vector of the dual
2322 * problem. The rest of the class is rather trivial.
2323 *
2324
2325 *
2326 * Since both primal and dual solver will use the same triangulation, but
2327 * different discretizations, it now becomes clear why we have made the
2328 * <code>Base</code> class a virtual one: since the final class will be
2329 * derived from both <code>PrimalSolver</code> as well as
2330 * <code>DualSolver</code>, it would have two <code>Base</code> instances,
2331 * would we not have marked the inheritance as virtual. Since in many
2332 * applications the base class would store much more information than just
2333 * the triangulation which needs to be shared between primal and dual
2334 * solvers, we do not usually want to use two such base classes.
2335 *
2336 * @code
2337 *   template <int dim>
2338 *   class DualSolver : public Solver<dim>
2339 *   {
2340 *   public:
2341 *   DualSolver(
2342 *   Triangulation<dim> & triangulation,
2343 *   const FiniteElement<dim> & fe,
2344 *   const Quadrature<dim> & quadrature,
2345 *   const Quadrature<dim - 1> & face_quadrature,
2346 *   const DualFunctional::DualFunctionalBase<dim> &dual_functional);
2347 *  
2348 *   protected:
2349 *   const SmartPointer<const DualFunctional::DualFunctionalBase<dim>>
2350 *   dual_functional;
2351 *   virtual void assemble_rhs(Vector<double> &rhs) const override;
2352 *  
2353 *   static const Functions::ZeroFunction<dim> boundary_values;
2354 *   };
2355 *  
2356 *   template <int dim>
2357 *   const Functions::ZeroFunction<dim> DualSolver<dim>::boundary_values;
2358 *  
2359 *   template <int dim>
2360 *   DualSolver<dim>::DualSolver(
2361 *   Triangulation<dim> & triangulation,
2362 *   const FiniteElement<dim> & fe,
2363 *   const Quadrature<dim> & quadrature,
2364 *   const Quadrature<dim - 1> & face_quadrature,
2365 *   const DualFunctional::DualFunctionalBase<dim> &dual_functional)
2366 *   : Base<dim>(triangulation)
2367 *   , Solver<dim>(triangulation,
2368 *   fe,
2369 *   quadrature,
2370 *   face_quadrature,
2371 *   boundary_values)
2372 *   , dual_functional(&dual_functional)
2373 *   {}
2374 *  
2375 *  
2376 *  
2377 *   template <int dim>
2378 *   void DualSolver<dim>::assemble_rhs(Vector<double> &rhs) const
2379 *   {
2380 *   dual_functional->assemble_rhs(this->dof_handler, rhs);
2381 *   }
2382 *  
2383 *  
2384 * @endcode
2385 *
2386 *
2387 * <a name="TheWeightedResidualclass"></a>
2388 * <h4>The WeightedResidual class</h4>
2389 *
2390
2391 *
2392 * Here finally comes the main class of this program, the one that
2393 * implements the dual weighted residual error estimator. It joins the
2394 * primal and dual solver classes to use them for the computation of
2395 * primal and dual solutions, and implements the error representation
2396 * formula for use as error estimate and mesh refinement.
2397 *
2398
2399 *
2400 * The first few of the functions of this class are mostly overriders of
2401 * the respective functions of the base class:
2402 *
2403 * @code
2404 *   template <int dim>
2405 *   class WeightedResidual : public PrimalSolver<dim>, public DualSolver<dim>
2406 *   {
2407 *   public:
2408 *   WeightedResidual(
2409 *   Triangulation<dim> & coarse_grid,
2410 *   const FiniteElement<dim> & primal_fe,
2411 *   const FiniteElement<dim> & dual_fe,
2412 *   const Quadrature<dim> & quadrature,
2413 *   const Quadrature<dim - 1> & face_quadrature,
2414 *   const Function<dim> & rhs_function,
2415 *   const Function<dim> & boundary_values,
2416 *   const DualFunctional::DualFunctionalBase<dim> &dual_functional);
2417 *  
2418 *   virtual void solve_problem() override;
2419 *  
2420 *   virtual void postprocess(
2421 *   const Evaluation::EvaluationBase<dim> &postprocessor) const override;
2422 *  
2423 *   virtual unsigned int n_dofs() const override;
2424 *  
2425 *   virtual void refine_grid() override;
2426 *  
2427 *   virtual void output_solution() const override;
2428 *  
2429 *   private:
2430 * @endcode
2431 *
2432 * In the private section, we have two functions that are used to call
2433 * the <code>solve_problem</code> functions of the primal and dual base
2434 * classes. These two functions will be called in parallel by the
2435 * <code>solve_problem</code> function of this class.
2436 *
2437 * @code
2438 *   void solve_primal_problem();
2439 *   void solve_dual_problem();
2440 * @endcode
2441 *
2442 * Then declare abbreviations for active cell iterators, to avoid that
2443 * we have to write this lengthy name over and over again:
2444 *
2445
2446 *
2447 *
2448 * @code
2449 *   using active_cell_iterator =
2450 *   typename DoFHandler<dim>::active_cell_iterator;
2451 *  
2452 * @endcode
2453 *
2454 * Next, declare a data type that we will us to store the contribution
2455 * of faces to the error estimator. The idea is that we can compute the
2456 * face terms from each of the two cells to this face, as they are the
2457 * same when viewed from both sides. What we will do is to compute them
2458 * only once, based on some rules explained below which of the two
2459 * adjacent cells will be in charge to do so. We then store the
2460 * contribution of each face in a map mapping faces to their values, and
2461 * only collect the contributions for each cell by looping over the
2462 * cells a second time and grabbing the values from the map.
2463 *
2464
2465 *
2466 * The data type of this map is declared here:
2467 *
2468 * @code
2469 *   using FaceIntegrals =
2470 *   typename std::map<typename DoFHandler<dim>::face_iterator, double>;
2471 *  
2472 * @endcode
2473 *
2474 * In the computation of the error estimates on cells and faces, we need
2475 * a number of helper objects, such as <code>FEValues</code> and
2476 * <code>FEFaceValues</code> functions, but also temporary objects
2477 * storing the values and gradients of primal and dual solutions, for
2478 * example. These fields are needed in the three functions that do the
2479 * integration on cells, and regular and irregular faces, respectively.
2480 *
2481
2482 *
2483 * There are three reasonable ways to provide these fields: first, as
2484 * local variables in the function that needs them; second, as member
2485 * variables of this class; third, as arguments passed to that function.
2486 *
2487
2488 *
2489 * These three alternatives all have drawbacks: the third that their
2490 * number is not negligible and would make calling these functions a
2491 * lengthy enterprise. The second has the drawback that it disallows
2492 * parallelization, since the threads that will compute the error
2493 * estimate have to have their own copies of these variables each, so
2494 * member variables of the enclosing class will not work. The first
2495 * approach, although straightforward, has a subtle but important
2496 * drawback: we will call these functions over and over again, many
2497 * thousands of times maybe; it now turns out that allocating
2498 * vectors and other objects that need memory from the heap is an
2499 * expensive business in terms of run-time, since memory allocation is
2500 * expensive when several threads are involved. It is thus
2501 * significantly better to allocate the memory only once, and recycle
2502 * the objects as often as possible.
2503 *
2504
2505 *
2506 * What to do? Our answer is to use a variant of the third strategy.
2507 * In fact, this is exactly what the WorkStream concept is supposed to
2508 * do (we have already introduced it above, but see also @ref threads).
2509 * To avoid that we have to give these functions a dozen or so
2510 * arguments, we pack all these variables into two structures, one which
2511 * is used for the computations on cells, the other doing them on the
2512 * faces. Both are then joined into the WeightedResidualScratchData class
2513 * that will serve as the "scratch data" class of the WorkStream concept:
2514 *
2515 * @code
2516 *   struct CellData
2517 *   {
2518 *   FEValues<dim> fe_values;
2519 *   const SmartPointer<const Function<dim>> right_hand_side;
2520 *  
2521 *   std::vector<double> cell_residual;
2522 *   std::vector<double> rhs_values;
2523 *   std::vector<double> dual_weights;
2524 *   std::vector<double> cell_laplacians;
2525 *   CellData(const FiniteElement<dim> &fe,
2526 *   const Quadrature<dim> & quadrature,
2527 *   const Function<dim> & right_hand_side);
2528 *   CellData(const CellData &cell_data);
2529 *   };
2530 *  
2531 *   struct FaceData
2532 *   {
2533 *   FEFaceValues<dim> fe_face_values_cell;
2534 *   FEFaceValues<dim> fe_face_values_neighbor;
2535 *   FESubfaceValues<dim> fe_subface_values_cell;
2536 *  
2537 *   std::vector<double> jump_residual;
2538 *   std::vector<double> dual_weights;
2539 *   typename std::vector<Tensor<1, dim>> cell_grads;
2540 *   typename std::vector<Tensor<1, dim>> neighbor_grads;
2541 *   FaceData(const FiniteElement<dim> & fe,
2542 *   const Quadrature<dim - 1> &face_quadrature);
2543 *   FaceData(const FaceData &face_data);
2544 *   };
2545 *  
2546 *   struct WeightedResidualScratchData
2547 *   {
2548 *   WeightedResidualScratchData(
2549 *   const FiniteElement<dim> & primal_fe,
2550 *   const Quadrature<dim> & primal_quadrature,
2551 *   const Quadrature<dim - 1> &primal_face_quadrature,
2552 *   const Function<dim> & rhs_function,
2553 *   const Vector<double> & primal_solution,
2554 *   const Vector<double> & dual_weights);
2555 *  
2556 *   WeightedResidualScratchData(
2557 *   const WeightedResidualScratchData &scratch_data);
2558 *  
2559 *   CellData cell_data;
2560 *   FaceData face_data;
2561 *   Vector<double> primal_solution;
2562 *   Vector<double> dual_weights;
2563 *   };
2564 *  
2565 *  
2566 * @endcode
2567 *
2568 * WorkStream::run generally wants both a scratch object and a copy
2569 * object. Here, for reasons similar to what we had in @ref step_9 "step-9" when
2570 * discussing the computation of an approximation of the gradient, we
2571 * don't actually need a "copy data" structure. Since WorkStream insists
2572 * on having one of these, we just declare an empty structure that does
2573 * nothing other than being there.
2574 *
2575 * @code
2576 *   struct WeightedResidualCopyData
2577 *   {};
2578 *  
2579 *  
2580 *  
2581 * @endcode
2582 *
2583 * Regarding the evaluation of the error estimator, we have one driver
2584 * function that uses WorkStream::run() to call the second function on
2585 * every cell:
2586 *
2587 * @code
2588 *   void estimate_error(Vector<float> &error_indicators) const;
2589 *  
2590 *   void estimate_on_one_cell(const active_cell_iterator & cell,
2591 *   WeightedResidualScratchData &scratch_data,
2592 *   WeightedResidualCopyData & copy_data,
2593 *   Vector<float> & error_indicators,
2594 *   FaceIntegrals &face_integrals) const;
2595 *  
2596 * @endcode
2597 *
2598 * Then we have functions that do the actual integration of the error
2599 * representation formula. They will treat the terms on the cell
2600 * interiors, on those faces that have no hanging nodes, and on those
2601 * faces with hanging nodes, respectively:
2602 *
2603 * @code
2604 *   void integrate_over_cell(const active_cell_iterator &cell,
2605 *   const Vector<double> & primal_solution,
2606 *   const Vector<double> & dual_weights,
2607 *   CellData & cell_data,
2608 *   Vector<float> &error_indicators) const;
2609 *  
2610 *   void integrate_over_regular_face(const active_cell_iterator &cell,
2611 *   const unsigned int face_no,
2612 *   const Vector<double> &primal_solution,
2613 *   const Vector<double> &dual_weights,
2614 *   FaceData & face_data,
2615 *   FaceIntegrals &face_integrals) const;
2616 *   void integrate_over_irregular_face(const active_cell_iterator &cell,
2617 *   const unsigned int face_no,
2618 *   const Vector<double> &primal_solution,
2619 *   const Vector<double> &dual_weights,
2620 *   FaceData & face_data,
2621 *   FaceIntegrals &face_integrals) const;
2622 *   };
2623 *  
2624 *  
2625 *  
2626 * @endcode
2627 *
2628 * In the implementation of this class, we first have the constructors of
2629 * the <code>CellData</code> and <code>FaceData</code> member classes, and
2630 * the <code>WeightedResidual</code> constructor. They only initialize
2631 * fields to their correct lengths, so we do not have to discuss them in
2632 * too much detail:
2633 *
2634 * @code
2635 *   template <int dim>
2636 *   WeightedResidual<dim>::CellData::CellData(
2637 *   const FiniteElement<dim> &fe,
2638 *   const Quadrature<dim> & quadrature,
2639 *   const Function<dim> & right_hand_side)
2640 *   : fe_values(fe,
2641 *   quadrature,
2643 *   update_JxW_values)
2644 *   , right_hand_side(&right_hand_side)
2645 *   , cell_residual(quadrature.size())
2646 *   , rhs_values(quadrature.size())
2647 *   , dual_weights(quadrature.size())
2648 *   , cell_laplacians(quadrature.size())
2649 *   {}
2650 *  
2651 *  
2652 *  
2653 *   template <int dim>
2654 *   WeightedResidual<dim>::CellData::CellData(const CellData &cell_data)
2655 *   : fe_values(cell_data.fe_values.get_fe(),
2656 *   cell_data.fe_values.get_quadrature(),
2658 *   update_JxW_values)
2659 *   , right_hand_side(cell_data.right_hand_side)
2660 *   , cell_residual(cell_data.cell_residual)
2661 *   , rhs_values(cell_data.rhs_values)
2662 *   , dual_weights(cell_data.dual_weights)
2663 *   , cell_laplacians(cell_data.cell_laplacians)
2664 *   {}
2665 *  
2666 *  
2667 *  
2668 *   template <int dim>
2669 *   WeightedResidual<dim>::FaceData::FaceData(
2670 *   const FiniteElement<dim> & fe,
2671 *   const Quadrature<dim - 1> &face_quadrature)
2672 *   : fe_face_values_cell(fe,
2673 *   face_quadrature,
2676 *   , fe_face_values_neighbor(fe,
2677 *   face_quadrature,
2680 *   , fe_subface_values_cell(fe, face_quadrature, update_gradients)
2681 *   {
2682 *   const unsigned int n_face_q_points = face_quadrature.size();
2683 *  
2684 *   jump_residual.resize(n_face_q_points);
2685 *   dual_weights.resize(n_face_q_points);
2686 *   cell_grads.resize(n_face_q_points);
2687 *   neighbor_grads.resize(n_face_q_points);
2688 *   }
2689 *  
2690 *  
2691 *  
2692 *   template <int dim>
2693 *   WeightedResidual<dim>::FaceData::FaceData(const FaceData &face_data)
2694 *   : fe_face_values_cell(face_data.fe_face_values_cell.get_fe(),
2695 *   face_data.fe_face_values_cell.get_quadrature(),
2698 *   , fe_face_values_neighbor(
2699 *   face_data.fe_face_values_neighbor.get_fe(),
2700 *   face_data.fe_face_values_neighbor.get_quadrature(),
2703 *   , fe_subface_values_cell(
2704 *   face_data.fe_subface_values_cell.get_fe(),
2705 *   face_data.fe_subface_values_cell.get_quadrature(),
2706 *   update_gradients)
2707 *   , jump_residual(face_data.jump_residual)
2708 *   , dual_weights(face_data.dual_weights)
2709 *   , cell_grads(face_data.cell_grads)
2710 *   , neighbor_grads(face_data.neighbor_grads)
2711 *   {}
2712 *  
2713 *  
2714 *  
2715 *   template <int dim>
2716 *   WeightedResidual<dim>::WeightedResidualScratchData::
2717 *   WeightedResidualScratchData(
2718 *   const FiniteElement<dim> & primal_fe,
2719 *   const Quadrature<dim> & primal_quadrature,
2720 *   const Quadrature<dim - 1> &primal_face_quadrature,
2721 *   const Function<dim> & rhs_function,
2722 *   const Vector<double> & primal_solution,
2723 *   const Vector<double> & dual_weights)
2724 *   : cell_data(primal_fe, primal_quadrature, rhs_function)
2725 *   , face_data(primal_fe, primal_face_quadrature)
2726 *   , primal_solution(primal_solution)
2727 *   , dual_weights(dual_weights)
2728 *   {}
2729 *  
2730 *   template <int dim>
2731 *   WeightedResidual<dim>::WeightedResidualScratchData::
2732 *   WeightedResidualScratchData(
2733 *   const WeightedResidualScratchData &scratch_data)
2734 *   : cell_data(scratch_data.cell_data)
2735 *   , face_data(scratch_data.face_data)
2736 *   , primal_solution(scratch_data.primal_solution)
2737 *   , dual_weights(scratch_data.dual_weights)
2738 *   {}
2739 *  
2740 *  
2741 *  
2742 *   template <int dim>
2743 *   WeightedResidual<dim>::WeightedResidual(
2744 *   Triangulation<dim> & coarse_grid,
2745 *   const FiniteElement<dim> & primal_fe,
2746 *   const FiniteElement<dim> & dual_fe,
2747 *   const Quadrature<dim> & quadrature,
2748 *   const Quadrature<dim - 1> & face_quadrature,
2749 *   const Function<dim> & rhs_function,
2750 *   const Function<dim> & bv,
2751 *   const DualFunctional::DualFunctionalBase<dim> &dual_functional)
2752 *   : Base<dim>(coarse_grid)
2753 *   , PrimalSolver<dim>(coarse_grid,
2754 *   primal_fe,
2755 *   quadrature,
2756 *   face_quadrature,
2757 *   rhs_function,
2758 *   bv)
2759 *   , DualSolver<dim>(coarse_grid,
2760 *   dual_fe,
2761 *   quadrature,
2762 *   face_quadrature,
2763 *   dual_functional)
2764 *   {}
2765 *  
2766 *  
2767 * @endcode
2768 *
2769 * The next five functions are boring, as they simply relay their work to
2770 * the base classes. The first calls the primal and dual solvers in
2771 * parallel, while postprocessing the solution and retrieving the number
2772 * of degrees of freedom is done by the primal class.
2773 *
2774 * @code
2775 *   template <int dim>
2776 *   void WeightedResidual<dim>::solve_problem()
2777 *   {
2778 *   Threads::TaskGroup<void> tasks;
2779 *   tasks +=
2780 *   Threads::new_task(&WeightedResidual<dim>::solve_primal_problem, *this);
2781 *   tasks +=
2782 *   Threads::new_task(&WeightedResidual<dim>::solve_dual_problem, *this);
2783 *   tasks.join_all();
2784 *   }
2785 *  
2786 *  
2787 *   template <int dim>
2788 *   void WeightedResidual<dim>::solve_primal_problem()
2789 *   {
2790 *   PrimalSolver<dim>::solve_problem();
2791 *   }
2792 *  
2793 *   template <int dim>
2794 *   void WeightedResidual<dim>::solve_dual_problem()
2795 *   {
2796 *   DualSolver<dim>::solve_problem();
2797 *   }
2798 *  
2799 *  
2800 *   template <int dim>
2801 *   void WeightedResidual<dim>::postprocess(
2802 *   const Evaluation::EvaluationBase<dim> &postprocessor) const
2803 *   {
2804 *   PrimalSolver<dim>::postprocess(postprocessor);
2805 *   }
2806 *  
2807 *  
2808 *   template <int dim>
2809 *   unsigned int WeightedResidual<dim>::n_dofs() const
2810 *   {
2811 *   return PrimalSolver<dim>::n_dofs();
2812 *   }
2813 *  
2814 *  
2815 *  
2816 * @endcode
2817 *
2818 * Now, it is becoming more interesting: the <code>refine_grid()</code>
2819 * function asks the error estimator to compute the cell-wise error
2820 * indicators, then uses their absolute values for mesh refinement.
2821 *
2822 * @code
2823 *   template <int dim>
2824 *   void WeightedResidual<dim>::refine_grid()
2825 *   {
2826 * @endcode
2827 *
2828 * First call the function that computes the cell-wise and global error:
2829 *
2830 * @code
2831 *   Vector<float> error_indicators(this->triangulation->n_active_cells());
2832 *   estimate_error(error_indicators);
2833 *  
2834 * @endcode
2835 *
2836 * Then note that marking cells for refinement or coarsening only works
2837 * if all indicators are positive, to allow their comparison. Thus, drop
2838 * the signs on all these indicators:
2839 *
2840 * @code
2841 *   for (float &error_indicator : error_indicators)
2842 *   error_indicator = std::fabs(error_indicator);
2843 *  
2844 * @endcode
2845 *
2846 * Finally, we can select between different strategies for
2847 * refinement. The default here is to refine those cells with the
2848 * largest error indicators that make up for a total of 80 per cent of
2849 * the error, while we coarsen those with the smallest indicators that
2850 * make up for the bottom 2 per cent of the error.
2851 *
2852 * @code
2854 *   error_indicators,
2855 *   0.8,
2856 *   0.02);
2857 *   this->triangulation->execute_coarsening_and_refinement();
2858 *   }
2859 *  
2860 *  
2861 * @endcode
2862 *
2863 * Since we want to output both the primal and the dual solution, we
2864 * overload the <code>output_solution</code> function. The only
2865 * interesting feature of this function is that the primal and dual
2866 * solutions are defined on different finite element spaces, which is not
2867 * the format the <code>DataOut</code> class expects. Thus, we have to
2868 * transfer them to a common finite element space. Since we want the
2869 * solutions only to see them qualitatively, we contend ourselves with
2870 * interpolating the dual solution to the (smaller) primal space. For the
2871 * interpolation, there is a library function, that takes a
2872 * AffineConstraints object including the hanging node
2873 * constraints. The rest is standard.
2874 *
2875 * @code
2876 *   template <int dim>
2877 *   void WeightedResidual<dim>::output_solution() const
2878 *   {
2879 *   AffineConstraints<double> primal_hanging_node_constraints;
2880 *   DoFTools::make_hanging_node_constraints(PrimalSolver<dim>::dof_handler,
2881 *   primal_hanging_node_constraints);
2882 *   primal_hanging_node_constraints.close();
2883 *   Vector<double> dual_solution(PrimalSolver<dim>::dof_handler.n_dofs());
2884 *   FETools::interpolate(DualSolver<dim>::dof_handler,
2885 *   DualSolver<dim>::solution,
2886 *   PrimalSolver<dim>::dof_handler,
2887 *   primal_hanging_node_constraints,
2888 *   dual_solution);
2889 *  
2890 *   DataOut<dim> data_out;
2891 *   data_out.attach_dof_handler(PrimalSolver<dim>::dof_handler);
2892 *  
2893 * @endcode
2894 *
2895 * Add the data vectors for which we want output. Add them both, the
2896 * <code>DataOut</code> functions can handle as many data vectors as you
2897 * wish to write to output:
2898 *
2899 * @code
2900 *   data_out.add_data_vector(PrimalSolver<dim>::solution, "primal_solution");
2901 *   data_out.add_data_vector(dual_solution, "dual_solution");
2902 *  
2903 *   data_out.build_patches();
2904 *  
2905 *   std::ofstream out("solution-" + std::to_string(this->refinement_cycle) +
2906 *   ".vtu");
2907 *   data_out.write(out, DataOutBase::vtu);
2908 *   }
2909 *  
2910 *  
2911 * @endcode
2912 *
2913 *
2914 * <a name="Estimatingerrors"></a>
2915 * <h3>Estimating errors</h3>
2916 *
2917
2918 *
2919 *
2920 * <a name="Errorestimationdriverfunctions"></a>
2921 * <h4>Error estimation driver functions</h4>
2922 *
2923
2924 *
2925 * As for the actual computation of error estimates, let's start with the
2926 * function that drives all this, i.e. calls those functions that actually
2927 * do the work, and finally collects the results.
2928 *
2929 * @code
2930 *   template <int dim>
2931 *   void
2932 *   WeightedResidual<dim>::estimate_error(Vector<float> &error_indicators) const
2933 *   {
2934 * @endcode
2935 *
2936 * The first task in computing the error is to set up vectors that
2937 * denote the primal solution, and the weights (z-z_h)=(z-I_hz), both in
2938 * the finite element space for which we have computed the dual
2939 * solution. For this, we have to interpolate the primal solution to the
2940 * dual finite element space, and to subtract the interpolation of the
2941 * computed dual solution to the primal finite element
2942 * space. Fortunately, the library provides functions for the
2943 * interpolation into larger or smaller finite element spaces, so this
2944 * is mostly obvious.
2945 *
2946
2947 *
2948 * First, let's do that for the primal solution: it is cell-wise
2949 * interpolated into the finite element space in which we have solved
2950 * the dual problem: But, again as in the
2951 * <code>WeightedResidual::output_solution</code> function we first need
2952 * to create an AffineConstraints object including the hanging node
2953 * constraints, but this time of the dual finite element space.
2954 *
2955 * @code
2956 *   AffineConstraints<double> dual_hanging_node_constraints;
2957 *   DoFTools::make_hanging_node_constraints(DualSolver<dim>::dof_handler,
2958 *   dual_hanging_node_constraints);
2959 *   dual_hanging_node_constraints.close();
2960 *   Vector<double> primal_solution(DualSolver<dim>::dof_handler.n_dofs());
2961 *   FETools::interpolate(PrimalSolver<dim>::dof_handler,
2962 *   PrimalSolver<dim>::solution,
2963 *   DualSolver<dim>::dof_handler,
2964 *   dual_hanging_node_constraints,
2965 *   primal_solution);
2966 *  
2967 * @endcode
2968 *
2969 * Then for computing the interpolation of the numerically approximated
2970 * dual solution z into the finite element space of the primal solution
2971 * and subtracting it from z: use the
2972 * <code>interpolate_difference</code> function, that gives (z-I_hz) in
2973 * the element space of the dual solution.
2974 *
2975 * @code
2976 *   AffineConstraints<double> primal_hanging_node_constraints;
2977 *   DoFTools::make_hanging_node_constraints(PrimalSolver<dim>::dof_handler,
2978 *   primal_hanging_node_constraints);
2979 *   primal_hanging_node_constraints.close();
2980 *   Vector<double> dual_weights(DualSolver<dim>::dof_handler.n_dofs());
2981 *   FETools::interpolation_difference(DualSolver<dim>::dof_handler,
2982 *   dual_hanging_node_constraints,
2983 *   DualSolver<dim>::solution,
2984 *   PrimalSolver<dim>::dof_handler,
2985 *   primal_hanging_node_constraints,
2986 *   dual_weights);
2987 *  
2988 * @endcode
2989 *
2990 * Note that this could probably have been more efficient since those
2991 * constraints have been used previously when assembling matrix and
2992 * right hand side for the primal problem and writing out the dual
2993 * solution. We leave the optimization of the program in this respect as
2994 * an exercise.
2995 *
2996
2997 *
2998 * Having computed the dual weights we now proceed with computing the
2999 * cell and face residuals of the primal solution. First we set up a map
3000 * between face iterators and their jump term contributions of faces to
3001 * the error estimator. The reason is that we compute the jump terms
3002 * only once, from one side of the face, and want to collect them only
3003 * afterwards when looping over all cells a second time.
3004 *
3005
3006 *
3007 * We initialize this map already with a value of -1e20 for all faces,
3008 * since this value will stand out in the results if something should go
3009 * wrong and we fail to compute the value for a face for some
3010 * reason. Secondly, this initialization already makes the std::map
3011 * object allocate all objects it may possibly need. This is important
3012 * since we will write into this structure from parallel threads,
3013 * and doing so would not be thread-safe if the map needed to allocate
3014 * memory and thereby reshape its data structures. In other words, the
3015 * initial initialization relieves us from the necessity to synchronize
3016 * the threads through a mutex each time they write to (and modify the
3017 * structure of) this map.
3018 *
3019 * @code
3020 *   FaceIntegrals face_integrals;
3021 *   for (const auto &cell :
3022 *   DualSolver<dim>::dof_handler.active_cell_iterators())
3023 *   for (const auto &face : cell->face_iterators())
3024 *   face_integrals[face] = -1e20;
3025 *  
3026 *   auto worker = [this,
3027 *   &error_indicators,
3028 *   &face_integrals](const active_cell_iterator & cell,
3029 *   WeightedResidualScratchData &scratch_data,
3030 *   WeightedResidualCopyData & copy_data) {
3031 *   this->estimate_on_one_cell(
3032 *   cell, scratch_data, copy_data, error_indicators, face_integrals);
3033 *   };
3034 *  
3035 *   auto do_nothing_copier =
3036 *   std::function<void(const WeightedResidualCopyData &)>();
3037 *  
3038 * @endcode
3039 *
3040 * Then hand it all off to WorkStream::run() to compute the
3041 * estimators for all cells in parallel:
3042 *
3043 * @code
3044 *   WorkStream::run(
3045 *   DualSolver<dim>::dof_handler.begin_active(),
3046 *   DualSolver<dim>::dof_handler.end(),
3047 *   worker,
3048 *   do_nothing_copier,
3049 *   WeightedResidualScratchData(*DualSolver<dim>::fe,
3050 *   *DualSolver<dim>::quadrature,
3051 *   *DualSolver<dim>::face_quadrature,
3052 *   *this->rhs_function,
3053 *   primal_solution,
3054 *   dual_weights),
3055 *   WeightedResidualCopyData());
3056 *  
3057 * @endcode
3058 *
3059 * Once the error contributions are computed, sum them up. For this,
3060 * note that the cell terms are already set, and that only the edge
3061 * terms need to be collected. Thus, loop over all cells and their
3062 * faces, make sure that the contributions of each of the faces are
3063 * there, and add them up. Only take minus one half of the jump term,
3064 * since the other half will be taken by the neighboring cell.
3065 *
3066 * @code
3067 *   unsigned int present_cell = 0;
3068 *   for (const auto &cell :
3069 *   DualSolver<dim>::dof_handler.active_cell_iterators())
3070 *   {
3071 *   for (const auto &face : cell->face_iterators())
3072 *   {
3073 *   Assert(face_integrals.find(face) != face_integrals.end(),
3074 *   ExcInternalError());
3075 *   error_indicators(present_cell) -= 0.5 * face_integrals[face];
3076 *   }
3077 *   ++present_cell;
3078 *   }
3079 *   std::cout << " Estimated error="
3080 *   << std::accumulate(error_indicators.begin(),
3081 *   error_indicators.end(),
3082 *   0.)
3083 *   << std::endl;
3084 *   }
3085 *  
3086 *  
3087 * @endcode
3088 *
3089 *
3090 * <a name="Estimatingonasinglecell"></a>
3091 * <h4>Estimating on a single cell</h4>
3092 *
3093
3094 *
3095 * Next we have the function that is called to estimate the error on a
3096 * single cell. The function may be called multiple times if the library was
3097 * configured to use multithreading. Here it goes:
3098 *
3099 * @code
3100 *   template <int dim>
3101 *   void WeightedResidual<dim>::estimate_on_one_cell(
3102 *   const active_cell_iterator & cell,
3103 *   WeightedResidualScratchData &scratch_data,
3104 *   WeightedResidualCopyData & copy_data,
3105 *   Vector<float> & error_indicators,
3106 *   FaceIntegrals & face_integrals) const
3107 *   {
3108 * @endcode
3109 *
3110 * Because of WorkStream, estimate_on_one_cell requires a CopyData object
3111 * even if it is no used. The next line silences a warning about this
3112 * unused variable.
3113 *
3114 * @code
3115 *   (void)copy_data;
3116 *  
3117 * @endcode
3118 *
3119 * First task on each cell is to compute the cell residual
3120 * contributions of this cell, and put them into the
3121 * <code>error_indicators</code> variable:
3122 *
3123 * @code
3124 *   integrate_over_cell(cell,
3125 *   scratch_data.primal_solution,
3126 *   scratch_data.dual_weights,
3127 *   scratch_data.cell_data,
3128 *   error_indicators);
3129 *  
3130 * @endcode
3131 *
3132 * After computing the cell terms, turn to the face terms. For this,
3133 * loop over all faces of the present cell, and see whether
3134 * something needs to be computed on it:
3135 *
3136 * @code
3137 *   for (const auto face_no : cell->face_indices())
3138 *   {
3139 * @endcode
3140 *
3141 * First, if this face is part of the boundary, then there is
3142 * nothing to do. However, to make things easier when summing up
3143 * the contributions of the faces of cells, we enter this face
3144 * into the list of faces with a zero contribution to the error.
3145 *
3146 * @code
3147 *   if (cell->face(face_no)->at_boundary())
3148 *   {
3149 *   face_integrals[cell->face(face_no)] = 0;
3150 *   continue;
3151 *   }
3152 *  
3153 * @endcode
3154 *
3155 * Next, note that since we want to compute the jump terms on
3156 * each face only once although we access it twice (if it is not
3157 * at the boundary), we have to define some rules who is
3158 * responsible for computing on a face:
3159 *
3160
3161 *
3162 * First, if the neighboring cell is on the same level as this
3163 * one, i.e. neither further refined not coarser, then the one
3164 * with the lower index within this level does the work. In
3165 * other words: if the other one has a lower index, then skip
3166 * work on this face:
3167 *
3168 * @code
3169 *   if ((cell->neighbor(face_no)->has_children() == false) &&
3170 *   (cell->neighbor(face_no)->level() == cell->level()) &&
3171 *   (cell->neighbor(face_no)->index() < cell->index()))
3172 *   continue;
3173 *  
3174 * @endcode
3175 *
3176 * Likewise, we always work from the coarser cell if this and
3177 * its neighbor differ in refinement. Thus, if the neighboring
3178 * cell is less refined than the present one, then do nothing
3179 * since we integrate over the subfaces when we visit the coarse
3180 * cell.
3181 *
3182 * @code
3183 *   if (cell->at_boundary(face_no) == false)
3184 *   if (cell->neighbor(face_no)->level() < cell->level())
3185 *   continue;
3186 *  
3187 *  
3188 * @endcode
3189 *
3190 * Now we know that we are in charge here, so actually compute
3191 * the face jump terms. If the face is a regular one, i.e. the
3192 * other side's cell is neither coarser not finer than this
3193 * cell, then call one function, and if the cell on the other
3194 * side is further refined, then use another function. Note that
3195 * the case that the cell on the other side is coarser cannot
3196 * happen since we have decided above that we handle this case
3197 * when we pass over that other cell.
3198 *
3199 * @code
3200 *   if (cell->face(face_no)->has_children() == false)
3201 *   integrate_over_regular_face(cell,
3202 *   face_no,
3203 *   scratch_data.primal_solution,
3204 *   scratch_data.dual_weights,
3205 *   scratch_data.face_data,
3206 *   face_integrals);
3207 *   else
3208 *   integrate_over_irregular_face(cell,
3209 *   face_no,
3210 *   scratch_data.primal_solution,
3211 *   scratch_data.dual_weights,
3212 *   scratch_data.face_data,
3213 *   face_integrals);
3214 *   }
3215 *   }
3216 *  
3217 *  
3218 * @endcode
3219 *
3220 *
3221 * <a name="Computingcelltermerrorcontributions"></a>
3222 * <h4>Computing cell term error contributions</h4>
3223 *
3224
3225 *
3226 * As for the actual computation of the error contributions, first turn to
3227 * the cell terms:
3228 *
3229 * @code
3230 *   template <int dim>
3231 *   void WeightedResidual<dim>::integrate_over_cell(
3232 *   const active_cell_iterator &cell,
3233 *   const Vector<double> & primal_solution,
3234 *   const Vector<double> & dual_weights,
3235 *   CellData & cell_data,
3236 *   Vector<float> & error_indicators) const
3237 *   {
3238 * @endcode
3239 *
3240 * The tasks to be done are what appears natural from looking at the
3241 * error estimation formula: first get the right hand side and Laplacian
3242 * of the numerical solution at the quadrature points for the cell
3243 * residual,
3244 *
3245 * @code
3246 *   cell_data.fe_values.reinit(cell);
3247 *   cell_data.right_hand_side->value_list(
3248 *   cell_data.fe_values.get_quadrature_points(), cell_data.rhs_values);
3249 *   cell_data.fe_values.get_function_laplacians(primal_solution,
3250 *   cell_data.cell_laplacians);
3251 *  
3252 * @endcode
3253 *
3254 * ...then get the dual weights...
3255 *
3256 * @code
3257 *   cell_data.fe_values.get_function_values(dual_weights,
3258 *   cell_data.dual_weights);
3259 *  
3260 * @endcode
3261 *
3262 * ...and finally build the sum over all quadrature points and store it
3263 * with the present cell:
3264 *
3265 * @code
3266 *   double sum = 0;
3267 *   for (unsigned int p = 0; p < cell_data.fe_values.n_quadrature_points; ++p)
3268 *   sum += ((cell_data.rhs_values[p] + cell_data.cell_laplacians[p]) *
3269 *   cell_data.dual_weights[p] * cell_data.fe_values.JxW(p));
3270 *   error_indicators(cell->active_cell_index()) += sum;
3271 *   }
3272 *  
3273 *  
3274 * @endcode
3275 *
3276 *
3277 * <a name="Computingedgetermerrorcontributions1"></a>
3278 * <h4>Computing edge term error contributions -- 1</h4>
3279 *
3280
3281 *
3282 * On the other hand, computation of the edge terms for the error estimate
3283 * is not so simple. First, we have to distinguish between faces with and
3284 * without hanging nodes. Because it is the simple case, we first consider
3285 * the case without hanging nodes on a face (let's call this the `regular'
3286 * case):
3287 *
3288 * @code
3289 *   template <int dim>
3290 *   void WeightedResidual<dim>::integrate_over_regular_face(
3291 *   const active_cell_iterator &cell,
3292 *   const unsigned int face_no,
3293 *   const Vector<double> & primal_solution,
3294 *   const Vector<double> & dual_weights,
3295 *   FaceData & face_data,
3296 *   FaceIntegrals & face_integrals) const
3297 *   {
3298 *   const unsigned int n_q_points =
3299 *   face_data.fe_face_values_cell.n_quadrature_points;
3300 *  
3301 * @endcode
3302 *
3303 * The first step is to get the values of the gradients at the
3304 * quadrature points of the finite element field on the present
3305 * cell. For this, initialize the <code>FEFaceValues</code> object
3306 * corresponding to this side of the face, and extract the gradients
3307 * using that object.
3308 *
3309 * @code
3310 *   face_data.fe_face_values_cell.reinit(cell, face_no);
3311 *   face_data.fe_face_values_cell.get_function_gradients(
3312 *   primal_solution, face_data.cell_grads);
3313 *  
3314 * @endcode
3315 *
3316 * The second step is then to extract the gradients of the finite
3317 * element solution at the quadrature points on the other side of the
3318 * face, i.e. from the neighboring cell.
3319 *
3320
3321 *
3322 * For this, do a sanity check before: make sure that the neighbor
3323 * actually exists (yes, we should not have come here if the neighbor
3324 * did not exist, but in complicated software there are bugs, so better
3325 * check this), and if this is not the case throw an error.
3326 *
3327 * @code
3328 *   Assert(cell->neighbor(face_no).state() == IteratorState::valid,
3329 *   ExcInternalError());
3330 * @endcode
3331 *
3332 * If we have that, then we need to find out with which face of the
3333 * neighboring cell we have to work, i.e. the <code>how-many'th</code> the
3334 * neighbor the present cell is of the cell behind the present face. For
3335 * this, there is a function, and we put the result into a variable with
3336 * the name <code>neighbor_neighbor</code>:
3337 *
3338 * @code
3339 *   const unsigned int neighbor_neighbor =
3340 *   cell->neighbor_of_neighbor(face_no);
3341 * @endcode
3342 *
3343 * Then define an abbreviation for the neighbor cell, initialize the
3344 * <code>FEFaceValues</code> object on that cell, and extract the
3345 * gradients on that cell:
3346 *
3347 * @code
3348 *   const active_cell_iterator neighbor = cell->neighbor(face_no);
3349 *   face_data.fe_face_values_neighbor.reinit(neighbor, neighbor_neighbor);
3350 *   face_data.fe_face_values_neighbor.get_function_gradients(
3351 *   primal_solution, face_data.neighbor_grads);
3352 *  
3353 * @endcode
3354 *
3355 * Now that we have the gradients on this and the neighboring cell,
3356 * compute the jump residual by multiplying the jump in the gradient
3357 * with the normal vector:
3358 *
3359 * @code
3360 *   for (unsigned int p = 0; p < n_q_points; ++p)
3361 *   face_data.jump_residual[p] =
3362 *   ((face_data.cell_grads[p] - face_data.neighbor_grads[p]) *
3363 *   face_data.fe_face_values_cell.normal_vector(p));
3364 *  
3365 * @endcode
3366 *
3367 * Next get the dual weights for this face:
3368 *
3369 * @code
3370 *   face_data.fe_face_values_cell.get_function_values(dual_weights,
3371 *   face_data.dual_weights);
3372 *  
3373 * @endcode
3374 *
3375 * Finally, we have to compute the sum over jump residuals, dual
3376 * weights, and quadrature weights, to get the result for this face:
3377 *
3378 * @code
3379 *   double face_integral = 0;
3380 *   for (unsigned int p = 0; p < n_q_points; ++p)
3381 *   face_integral +=
3382 *   (face_data.jump_residual[p] * face_data.dual_weights[p] *
3383 *   face_data.fe_face_values_cell.JxW(p));
3384 *  
3385 * @endcode
3386 *
3387 * Double check that the element already exists and that it was not
3388 * already written to...
3389 *
3390 * @code
3391 *   Assert(face_integrals.find(cell->face(face_no)) != face_integrals.end(),
3392 *   ExcInternalError());
3393 *   Assert(face_integrals[cell->face(face_no)] == -1e20, ExcInternalError());
3394 *  
3395 * @endcode
3396 *
3397 * ...then store computed value at assigned location. Note that the
3398 * stored value does not contain the factor 1/2 that appears in the
3399 * error representation. The reason is that the term actually does not
3400 * have this factor if we loop over all faces in the triangulation, but
3401 * only appears if we write it as a sum over all cells and all faces of
3402 * each cell; we thus visit the same face twice. We take account of this
3403 * by using this factor -1/2 later, when we sum up the contributions for
3404 * each cell individually.
3405 *
3406 * @code
3407 *   face_integrals[cell->face(face_no)] = face_integral;
3408 *   }
3409 *  
3410 *  
3411 * @endcode
3412 *
3413 *
3414 * <a name="Computingedgetermerrorcontributions2"></a>
3415 * <h4>Computing edge term error contributions -- 2</h4>
3416 *
3417
3418 *
3419 * We are still missing the case of faces with hanging nodes. This is what
3420 * is covered in this function:
3421 *
3422 * @code
3423 *   template <int dim>
3424 *   void WeightedResidual<dim>::integrate_over_irregular_face(
3425 *   const active_cell_iterator &cell,
3426 *   const unsigned int face_no,
3427 *   const Vector<double> & primal_solution,
3428 *   const Vector<double> & dual_weights,
3429 *   FaceData & face_data,
3430 *   FaceIntegrals & face_integrals) const
3431 *   {
3432 * @endcode
3433 *
3434 * First again two abbreviations, and some consistency checks whether
3435 * the function is called only on faces for which it is supposed to be
3436 * called:
3437 *
3438 * @code
3439 *   const unsigned int n_q_points =
3440 *   face_data.fe_face_values_cell.n_quadrature_points;
3441 *  
3442 *   const typename DoFHandler<dim>::face_iterator face = cell->face(face_no);
3443 *   const typename DoFHandler<dim>::cell_iterator neighbor =
3444 *   cell->neighbor(face_no);
3445 *   Assert(neighbor.state() == IteratorState::valid, ExcInternalError());
3446 *   Assert(neighbor->has_children(), ExcInternalError());
3447 *   (void)neighbor;
3448 *  
3449 * @endcode
3450 *
3451 * Then find out which neighbor the present cell is of the adjacent
3452 * cell. Note that we will operate on the children of this adjacent
3453 * cell, but that their orientation is the same as that of their mother,
3454 * i.e. the neighbor direction is the same.
3455 *
3456 * @code
3457 *   const unsigned int neighbor_neighbor =
3458 *   cell->neighbor_of_neighbor(face_no);
3459 *  
3460 * @endcode
3461 *
3462 * Then simply do everything we did in the previous function for one
3463 * face for all the sub-faces now:
3464 *
3465 * @code
3466 *   for (unsigned int subface_no = 0; subface_no < face->n_children();
3467 *   ++subface_no)
3468 *   {
3469 * @endcode
3470 *
3471 * Start with some checks again: get an iterator pointing to the
3472 * cell behind the present subface and check whether its face is a
3473 * subface of the one we are considering. If that were not the case,
3474 * then there would be either a bug in the
3475 * <code>neighbor_neighbor</code> function called above, or -- worse
3476 * -- some function in the library did not keep to some underlying
3477 * assumptions about cells, their children, and their faces. In any
3478 * case, even though this assertion should not be triggered, it does
3479 * not harm to be cautious, and in optimized mode computations the
3480 * assertion will be removed anyway.
3481 *
3482 * @code
3483 *   const active_cell_iterator neighbor_child =
3484 *   cell->neighbor_child_on_subface(face_no, subface_no);
3485 *   Assert(neighbor_child->face(neighbor_neighbor) ==
3486 *   cell->face(face_no)->child(subface_no),
3487 *   ExcInternalError());
3488 *  
3489 * @endcode
3490 *
3491 * Now start the work by again getting the gradient of the solution
3492 * first at this side of the interface,
3493 *
3494 * @code
3495 *   face_data.fe_subface_values_cell.reinit(cell, face_no, subface_no);
3496 *   face_data.fe_subface_values_cell.get_function_gradients(
3497 *   primal_solution, face_data.cell_grads);
3498 * @endcode
3499 *
3500 * then at the other side,
3501 *
3502 * @code
3503 *   face_data.fe_face_values_neighbor.reinit(neighbor_child,
3504 *   neighbor_neighbor);
3505 *   face_data.fe_face_values_neighbor.get_function_gradients(
3506 *   primal_solution, face_data.neighbor_grads);
3507 *  
3508 * @endcode
3509 *
3510 * and finally building the jump residuals. Since we take the normal
3511 * vector from the other cell this time, revert the sign of the
3512 * first term compared to the other function:
3513 *
3514 * @code
3515 *   for (unsigned int p = 0; p < n_q_points; ++p)
3516 *   face_data.jump_residual[p] =
3517 *   ((face_data.neighbor_grads[p] - face_data.cell_grads[p]) *
3518 *   face_data.fe_face_values_neighbor.normal_vector(p));
3519 *  
3520 * @endcode
3521 *
3522 * Then get dual weights:
3523 *
3524 * @code
3525 *   face_data.fe_face_values_neighbor.get_function_values(
3526 *   dual_weights, face_data.dual_weights);
3527 *  
3528 * @endcode
3529 *
3530 * At last, sum up the contribution of this sub-face, and set it in
3531 * the global map:
3532 *
3533 * @code
3534 *   double face_integral = 0;
3535 *   for (unsigned int p = 0; p < n_q_points; ++p)
3536 *   face_integral +=
3537 *   (face_data.jump_residual[p] * face_data.dual_weights[p] *
3538 *   face_data.fe_face_values_neighbor.JxW(p));
3539 *   face_integrals[neighbor_child->face(neighbor_neighbor)] =
3540 *   face_integral;
3541 *   }
3542 *  
3543 * @endcode
3544 *
3545 * Once the contributions of all sub-faces are computed, loop over all
3546 * sub-faces to collect and store them with the mother face for simple
3547 * use when later collecting the error terms of cells. Again make safety
3548 * checks that the entries for the sub-faces have been computed and do
3549 * not carry an invalid value.
3550 *
3551 * @code
3552 *   double sum = 0;
3553 *   for (unsigned int subface_no = 0; subface_no < face->n_children();
3554 *   ++subface_no)
3555 *   {
3556 *   Assert(face_integrals.find(face->child(subface_no)) !=
3557 *   face_integrals.end(),
3558 *   ExcInternalError());
3559 *   Assert(face_integrals[face->child(subface_no)] != -1e20,
3560 *   ExcInternalError());
3561 *  
3562 *   sum += face_integrals[face->child(subface_no)];
3563 *   }
3564 * @endcode
3565 *
3566 * Finally store the value with the parent face.
3567 *
3568 * @code
3569 *   face_integrals[face] = sum;
3570 *   }
3571 *  
3572 *   } // namespace LaplaceSolver
3573 *  
3574 *  
3575 * @endcode
3576 *
3577 *
3578 * <a name="Asimulationframework"></a>
3579 * <h3>A simulation framework</h3>
3580 *
3581
3582 *
3583 * In the previous example program, we have had two functions that were used
3584 * to drive the process of solving on subsequently finer grids. We extend
3585 * this here to allow for a number of parameters to be passed to these
3586 * functions, and put all of that into framework class.
3587 *
3588
3589 *
3590 * You will have noted that this program is built up of a number of small
3591 * parts (evaluation functions, solver classes implementing various
3592 * refinement methods, different dual functionals, different problem and
3593 * data descriptions), which makes the program relatively simple to extend,
3594 * but also allows to solve a large number of different problems by
3595 * replacing one part by another. We reflect this flexibility by declaring a
3596 * structure in the following framework class that holds a number of
3597 * parameters that may be set to test various combinations of the parts of
3598 * this program, and which can be used to test it at various problems and
3599 * discretizations in a simple way.
3600 *
3601 * @code
3602 *   template <int dim>
3603 *   struct Framework
3604 *   {
3605 *   public:
3606 * @endcode
3607 *
3608 * First, we declare two abbreviations for simple use of the respective
3609 * data types:
3610 *
3611 * @code
3612 *   using Evaluator = Evaluation::EvaluationBase<dim>;
3613 *   using EvaluatorList = std::list<Evaluator *>;
3614 *  
3615 *  
3616 * @endcode
3617 *
3618 * Then we have the structure which declares all the parameters that may
3619 * be set. In the default constructor of the structure, these values are
3620 * all set to default values, for simple use.
3621 *
3622 * @code
3623 *   struct ProblemDescription
3624 *   {
3625 * @endcode
3626 *
3627 * First allow for the degrees of the piecewise polynomials by which the
3628 * primal and dual problems will be discretized. They default to (bi-,
3629 * tri-)linear ansatz functions for the primal, and (bi-, tri-)quadratic
3630 * ones for the dual problem. If a refinement criterion is chosen that
3631 * does not need the solution of a dual problem, the value of the dual
3632 * finite element degree is of course ignored.
3633 *
3634 * @code
3635 *   unsigned int primal_fe_degree;
3636 *   unsigned int dual_fe_degree;
3637 *  
3638 * @endcode
3639 *
3640 * Then have an object that describes the problem type, i.e. right hand
3641 * side, domain, boundary values, etc. The pointer needed here defaults
3642 * to the Null pointer, i.e. you will have to set it in actual instances
3643 * of this object to make it useful.
3644 *
3645 * @code
3646 *   std::unique_ptr<const Data::SetUpBase<dim>> data;
3647 *  
3648 * @endcode
3649 *
3650 * Since we allow to use different refinement criteria (global
3651 * refinement, refinement by the Kelly error indicator, possibly with a
3652 * weight, and using the dual estimator), define a number of enumeration
3653 * values, and subsequently a variable of that type. It will default to
3654 * <code>dual_weighted_error_estimator</code>.
3655 *
3656 * @code
3657 *   enum RefinementCriterion
3658 *   {
3659 *   dual_weighted_error_estimator,
3660 *   global_refinement,
3661 *   kelly_indicator,
3662 *   weighted_kelly_indicator
3663 *   };
3664 *  
3665 *   RefinementCriterion refinement_criterion;
3666 *  
3667 * @endcode
3668 *
3669 * Next, an object that describes the dual functional. It is only needed
3670 * if the dual weighted residual refinement is chosen, and also defaults
3671 * to a Null pointer.
3672 *
3673 * @code
3674 *   std::unique_ptr<const DualFunctional::DualFunctionalBase<dim>>
3675 *   dual_functional;
3676 *  
3677 * @endcode
3678 *
3679 * Then a list of evaluation objects. Its default value is empty,
3680 * i.e. no evaluation objects.
3681 *
3682 * @code
3683 *   EvaluatorList evaluator_list;
3684 *  
3685 * @endcode
3686 *
3687 * Next to last, a function that is used as a weight to the
3688 * <code>RefinementWeightedKelly</code> class. The default value of this
3689 * pointer is zero, but you have to set it to some other value if you
3690 * want to use the <code>weighted_kelly_indicator</code> refinement
3691 * criterion.
3692 *
3693 * @code
3694 *   std::unique_ptr<const Function<dim>> kelly_weight;
3695 *  
3696 * @endcode
3697 *
3698 * Finally, we have a variable that denotes the maximum number of
3699 * degrees of freedom we allow for the (primal) discretization. If it is
3700 * exceeded, we stop the process of solving and intermittent mesh
3701 * refinement. Its default value is 20,000.
3702 *
3703 * @code
3704 *   unsigned int max_degrees_of_freedom;
3705 *  
3706 * @endcode
3707 *
3708 * Finally the default constructor of this class:
3709 *
3710 * @code
3711 *   ProblemDescription();
3712 *   };
3713 *  
3714 * @endcode
3715 *
3716 * The driver framework class only has one method which calls solver and
3717 * mesh refinement intermittently, and does some other small tasks in
3718 * between. Since it does not need data besides the parameters given to
3719 * it, we make it static:
3720 *
3721 * @code
3722 *   static void run(const ProblemDescription &descriptor);
3723 *   };
3724 *  
3725 *  
3726 * @endcode
3727 *
3728 * As for the implementation, first the constructor of the parameter object,
3729 * setting all values to their defaults:
3730 *
3731 * @code
3732 *   template <int dim>
3733 *   Framework<dim>::ProblemDescription::ProblemDescription()
3734 *   : primal_fe_degree(1)
3735 *   , dual_fe_degree(2)
3736 *   , refinement_criterion(dual_weighted_error_estimator)
3737 *   , max_degrees_of_freedom(20000)
3738 *   {}
3739 *  
3740 *  
3741 *  
3742 * @endcode
3743 *
3744 * Then the function which drives the whole process:
3745 *
3746 * @code
3747 *   template <int dim>
3748 *   void Framework<dim>::run(const ProblemDescription &descriptor)
3749 *   {
3750 * @endcode
3751 *
3752 * First create a triangulation from the given data object,
3753 *
3754 * @code
3757 *   descriptor.data->create_coarse_grid(triangulation);
3758 *  
3759 * @endcode
3760 *
3761 * then a set of finite elements and appropriate quadrature formula:
3762 *
3763 * @code
3764 *   const FE_Q<dim> primal_fe(descriptor.primal_fe_degree);
3765 *   const FE_Q<dim> dual_fe(descriptor.dual_fe_degree);
3766 *   const QGauss<dim> quadrature(descriptor.dual_fe_degree + 1);
3767 *   const QGauss<dim - 1> face_quadrature(descriptor.dual_fe_degree + 1);
3768 *  
3769 * @endcode
3770 *
3771 * Next, select one of the classes implementing different refinement
3772 * criteria.
3773 *
3774 * @code
3775 *   std::unique_ptr<LaplaceSolver::Base<dim>> solver;
3776 *   switch (descriptor.refinement_criterion)
3777 *   {
3778 *   case ProblemDescription::dual_weighted_error_estimator:
3779 *   {
3780 *   solver = std::make_unique<LaplaceSolver::WeightedResidual<dim>>(
3781 *   triangulation,
3782 *   primal_fe,
3783 *   dual_fe,
3784 *   quadrature,
3785 *   face_quadrature,
3786 *   descriptor.data->get_right_hand_side(),
3787 *   descriptor.data->get_boundary_values(),
3788 *   *descriptor.dual_functional);
3789 *   break;
3790 *   }
3791 *  
3792 *   case ProblemDescription::global_refinement:
3793 *   {
3794 *   solver = std::make_unique<LaplaceSolver::RefinementGlobal<dim>>(
3795 *   triangulation,
3796 *   primal_fe,
3797 *   quadrature,
3798 *   face_quadrature,
3799 *   descriptor.data->get_right_hand_side(),
3800 *   descriptor.data->get_boundary_values());
3801 *   break;
3802 *   }
3803 *  
3804 *   case ProblemDescription::kelly_indicator:
3805 *   {
3806 *   solver = std::make_unique<LaplaceSolver::RefinementKelly<dim>>(
3807 *   triangulation,
3808 *   primal_fe,
3809 *   quadrature,
3810 *   face_quadrature,
3811 *   descriptor.data->get_right_hand_side(),
3812 *   descriptor.data->get_boundary_values());
3813 *   break;
3814 *   }
3815 *  
3816 *   case ProblemDescription::weighted_kelly_indicator:
3817 *   {
3818 *   solver =
3819 *   std::make_unique<LaplaceSolver::RefinementWeightedKelly<dim>>(
3820 *   triangulation,
3821 *   primal_fe,
3822 *   quadrature,
3823 *   face_quadrature,
3824 *   descriptor.data->get_right_hand_side(),
3825 *   descriptor.data->get_boundary_values(),
3826 *   *descriptor.kelly_weight);
3827 *   break;
3828 *   }
3829 *  
3830 *   default:
3831 *   AssertThrow(false, ExcInternalError());
3832 *   }
3833 *  
3834 * @endcode
3835 *
3836 * Now that all objects are in place, run the main loop. The stopping
3837 * criterion is implemented at the bottom of the loop.
3838 *
3839
3840 *
3841 * In the loop, first set the new cycle number, then solve the problem,
3842 * output its solution(s), apply the evaluation objects to it, then decide
3843 * whether we want to refine the mesh further and solve again on this
3844 * mesh, or jump out of the loop.
3845 *
3846 * @code
3847 *   for (unsigned int step = 0; true; ++step)
3848 *   {
3849 *   std::cout << "Refinement cycle: " << step << std::endl;
3850 *  
3851 *   solver->set_refinement_cycle(step);
3852 *   solver->solve_problem();
3853 *   solver->output_solution();
3854 *  
3855 *   std::cout << " Number of degrees of freedom=" << solver->n_dofs()
3856 *   << std::endl;
3857 *  
3858 *   for (const auto &evaluator : descriptor.evaluator_list)
3859 *   {
3860 *   evaluator->set_refinement_cycle(step);
3861 *   solver->postprocess(*evaluator);
3862 *   }
3863 *  
3864 *  
3865 *   if (solver->n_dofs() < descriptor.max_degrees_of_freedom)
3866 *   solver->refine_grid();
3867 *   else
3868 *   break;
3869 *   }
3870 *  
3871 * @endcode
3872 *
3873 * Clean up the screen after the loop has run:
3874 *
3875 * @code
3876 *   std::cout << std::endl;
3877 *   }
3878 *  
3879 *   } // namespace Step14
3880 *  
3881 *  
3882 *  
3883 * @endcode
3884 *
3885 *
3886 * <a name="Themainfunction"></a>
3887 * <h3>The main function</h3>
3888 *
3889
3890 *
3891 * Here finally comes the main function. It drives the whole process by
3892 * specifying a set of parameters to be used for the simulation (polynomial
3893 * degrees, evaluation and dual functionals, etc), and passes them packed into
3894 * a structure to the frame work class above.
3895 *
3896 * @code
3897 *   int main()
3898 *   {
3899 *   try
3900 *   {
3901 *   using namespace Step14;
3902 *  
3903 * @endcode
3904 *
3905 * Describe the problem we want to solve here by passing a descriptor
3906 * object to the function doing the rest of the work:
3907 *
3908 * @code
3909 *   const unsigned int dim = 2;
3910 *   Framework<dim>::ProblemDescription descriptor;
3911 *  
3912 * @endcode
3913 *
3914 * First set the refinement criterion we wish to use:
3915 *
3916 * @code
3917 *   descriptor.refinement_criterion =
3918 *   Framework<dim>::ProblemDescription::dual_weighted_error_estimator;
3919 * @endcode
3920 *
3921 * Here, we could as well have used <code>global_refinement</code> or
3922 * <code>weighted_kelly_indicator</code>. Note that the information
3923 * given about dual finite elements, dual functional, etc is only
3924 * important for the given choice of refinement criterion, and is
3925 * ignored otherwise.
3926 *
3927
3928 *
3929 * Then set the polynomial degrees of primal and dual problem. We choose
3930 * here bi-linear and bi-quadratic ones:
3931 *
3932 * @code
3933 *   descriptor.primal_fe_degree = 1;
3934 *   descriptor.dual_fe_degree = 2;
3935 *  
3936 * @endcode
3937 *
3938 * Then set the description of the test case, i.e. domain, boundary
3939 * values, and right hand side. These are prepackaged in classes. We
3940 * take here the description of <code>Exercise_2_3</code>, but you can
3941 * also use <code>CurvedRidges@<dim@></code>:
3942 *
3943 * @code
3944 *   descriptor.data =
3945 *   std::make_unique<Data::SetUp<Data::Exercise_2_3<dim>, dim>>();
3946 *  
3947 * @endcode
3948 *
3949 * Next set first a dual functional, then a list of evaluation
3950 * objects. We choose as default the evaluation of the value at an
3951 * evaluation point, represented by the classes
3952 * <code>PointValueEvaluation</code> in the namespaces of evaluation and
3953 * dual functional classes. You can also set the
3954 * <code>PointXDerivativeEvaluation</code> classes for the x-derivative
3955 * instead of the value at the evaluation point.
3956 *
3957
3958 *
3959 * Note that dual functional and evaluation objects should
3960 * match. However, you can give as many evaluation functionals as you
3961 * want, so you can have both point value and derivative evaluated after
3962 * each step. One such additional evaluation is to output the grid in
3963 * each step.
3964 *
3965 * @code
3966 *   const Point<dim> evaluation_point(0.75, 0.75);
3967 *   descriptor.dual_functional =
3968 *   std::make_unique<DualFunctional::PointValueEvaluation<dim>>(
3969 *   evaluation_point);
3970 *  
3971 *   Evaluation::PointValueEvaluation<dim> postprocessor1(evaluation_point);
3972 *   Evaluation::GridOutput<dim> postprocessor2("grid");
3973 *  
3974 *   descriptor.evaluator_list.push_back(&postprocessor1);
3975 *   descriptor.evaluator_list.push_back(&postprocessor2);
3976 *  
3977 * @endcode
3978 *
3979 * Set the maximal number of degrees of freedom after which we want the
3980 * program to stop refining the mesh further:
3981 *
3982 * @code
3983 *   descriptor.max_degrees_of_freedom = 20000;
3984 *  
3985 * @endcode
3986 *
3987 * Finally pass the descriptor object to a function that runs the entire
3988 * solution with it:
3989 *
3990 * @code
3991 *   Framework<dim>::run(descriptor);
3992 *   }
3993 *  
3994 * @endcode
3995 *
3996 * Catch exceptions to give information about things that failed:
3997 *
3998 * @code
3999 *   catch (std::exception &exc)
4000 *   {
4001 *   std::cerr << std::endl
4002 *   << std::endl
4003 *   << "----------------------------------------------------"
4004 *   << std::endl;
4005 *   std::cerr << "Exception on processing: " << std::endl
4006 *   << exc.what() << std::endl
4007 *   << "Aborting!" << std::endl
4008 *   << "----------------------------------------------------"
4009 *   << std::endl;
4010 *   return 1;
4011 *   }
4012 *   catch (...)
4013 *   {
4014 *   std::cerr << std::endl
4015 *   << std::endl
4016 *   << "----------------------------------------------------"
4017 *   << std::endl;
4018 *   std::cerr << "Unknown exception!" << std::endl
4019 *   << "Aborting!" << std::endl
4020 *   << "----------------------------------------------------"
4021 *   << std::endl;
4022 *   return 1;
4023 *   }
4024 *  
4025 *   return 0;
4026 *   }
4027 * @endcode
4028<a name="Results"></a><h1>Results</h1>
4029
4030
4031<a name="Pointvalues"></a><h3>Point values</h3>
4032
4033
4034
4035This program offers a lot of possibilities to play around. We can thus
4036only show a small part of all possible results that can be obtained
4037with the help of this program. However, you are encouraged to just try
4038it out, by changing the settings in the main program. Here, we start
4039by simply letting it run, unmodified:
4040@code
4041Refinement cycle: 0
4042 Number of degrees of freedom=72
4043 Point value=0.03243
4044 Estimated error=0.000702385
4045Refinement cycle: 1
4046 Number of degrees of freedom=67
4047 Point value=0.0324827
4048 Estimated error=0.000888953
4049Refinement cycle: 2
4050 Number of degrees of freedom=130
4051 Point value=0.0329619
4052 Estimated error=0.000454606
4053Refinement cycle: 3
4054 Number of degrees of freedom=307
4055 Point value=0.0331934
4056 Estimated error=0.000241254
4057Refinement cycle: 4
4058 Number of degrees of freedom=718
4059 Point value=0.0333675
4060 Estimated error=7.4912e-05
4061Refinement cycle: 5
4062 Number of degrees of freedom=1665
4063 Point value=0.0334083
4064 Estimated error=3.69111e-05
4065Refinement cycle: 6
4066 Number of degrees of freedom=3975
4067 Point value=0.033431
4068 Estimated error=1.54218e-05
4069Refinement cycle: 7
4070 Number of degrees of freedom=8934
4071 Point value=0.0334406
4072 Estimated error=6.28359e-06
4073Refinement cycle: 8
4074 Number of degrees of freedom=21799
4075 Point value=0.0334444
4076@endcode
4077
4078
4079First let's look what the program actually computed. On the seventh
4080grid, primal and dual numerical solutions look like this (using a
4081color scheme intended to evoke the snow-capped mountains of
4082Colorado that the original author of this program now calls
4083home):
4084<table align="center">
4085 <tr>
4086 <td width="50%">
4087 <img src="https://www.dealii.org/images/steps/developer/step-14.point-value.solution-7.9.2.png" alt="">
4088 </td>
4089 <td width="50%">
4090 <img src="https://www.dealii.org/images/steps/developer/step-14.point-value.solution-7-dual.9.2.png" alt="">
4091 </td>
4092 </tr>
4093</table>
4094Apparently, the region at the bottom left is so unimportant for the
4095point value evaluation at the top right that the grid is left entirely
4096unrefined there, even though the solution has singularities at the inner
4097corner of that cell! Due
4098to the symmetry in right hand side and domain, the solution should
4099actually look like at the top right in all four corners, but the mesh
4100refinement criterion involving the dual solution chose to refine them
4101differently -- because we said that we really only care about a single
4102function value somewhere at the top right.
4103
4104
4105
4106Here are some of the meshes that are produced in refinement cycles 0,
41072, 4 (top row), and 5, 7, and 8 (bottom row):
4108
4109<table width="80%" align="center">
4110 <tr>
4111 <td><img src="https://www.dealii.org/images/steps/developer/step-14.point-value.grid-0.9.2.png" alt="" width="100%"></td>
4112 <td><img src="https://www.dealii.org/images/steps/developer/step-14.point-value.grid-2.9.2.png" alt="" width="100%"></td>
4113 <td><img src="https://www.dealii.org/images/steps/developer/step-14.point-value.grid-4.9.2.png" alt="" width="100%"></td>
4114 </tr>
4115 <tr>
4116 <td><img src="https://www.dealii.org/images/steps/developer/step-14.point-value.grid-5.9.2.png" alt="" width="100%"></td>
4117 <td><img src="https://www.dealii.org/images/steps/developer/step-14.point-value.grid-7.9.2.png" alt="" width="100%"></td>
4118 <td><img src="https://www.dealii.org/images/steps/developer/step-14.point-value.grid-8.9.2.png" alt="" width="100%"></td>
4119 </tr>
4120</table>
4121
4122Note the subtle interplay between resolving the corner singularities,
4123and resolving around the point of evaluation. It will be rather
4124difficult to generate such a mesh by hand, as this would involve to
4125judge quantitatively how much which of the four corner singularities
4126should be resolved, and to set the weight compared to the vicinity of
4127the evaluation point.
4128
4129
4130
4131The program prints the point value and the estimated error in this
4132quantity. From extrapolating it, we can guess that the exact value is
4133somewhere close to 0.0334473, plus or minus 0.0000001 (note that we get
4134almost 6 valid digits from only 22,000 (primal) degrees of
4135freedom. This number cannot be obtained from the value of the
4136functional alone, but I have used the assumption that the error
4137estimator is mostly exact, and extrapolated the computed value plus
4138the estimated error, to get an approximation of the true
4139value. Computing with more degrees of freedom shows that this
4140assumption is indeed valid.
4141
4142
4143
4144From the computed results, we can generate two graphs: one that shows
4145the convergence of the error @f$J(u)-J(u_h)@f$ (taking the
4146extrapolated value as correct) in the point value, and the value that
4147we get by adding up computed value @f$J(u_h)@f$ and estimated
4148error eta (if the error estimator @f$eta@f$ were exact, then the value
4149@f$J(u_h)+\eta@f$ would equal the exact point value, and the error
4150in this quantity would always be zero; however, since the error
4151estimator is only a - good - approximation to the true error, we can
4152by this only reduce the size of the error). In this graph, we also
4153indicate the complexity @f${\cal O}(1/N)@f$ to show that mesh refinement
4154acts optimal in this case. The second chart compares
4155true and estimated error, and shows that the two are actually very
4156close to each other, even for such a complicated quantity as the point
4157value:
4158
4159
4160<table width="80%" align="center">
4161 <tr>
4162 <td><img src="https://www.dealii.org/images/steps/developer/step-14.point-value.error.png" alt="" width="100%"></td>
4163 <td><img src="https://www.dealii.org/images/steps/developer/step-14.point-value.error-estimation.png" alt="" width="100%"></td>
4164 </tr>
4165</table>
4166
4167
4168<a name="Comparingrefinementcriteria"></a><h3>Comparing refinement criteria</h3>
4169
4170
4171
4172Since we have accepted quite some effort when using the mesh
4173refinement driven by the dual weighted error estimator (for solving
4174the dual problem, and for evaluating the error representation), it is
4175worth while asking whether that effort was successful. To this end, we
4176first compare the achieved error levels for different mesh refinement
4177criteria. To generate this data, simply change the value of the mesh
4178refinement criterion variable in the main program. The results are
4179thus (for the weight in the Kelly indicator, we have chosen the
4180function @f$1/(r^2+0.1^2)@f$, where @f$r@f$
4181is the distance to the evaluation point; it can be shown that this is
4182the optimal weight if we neglect the effects of boundaries):
4183
4184<img src="https://www.dealii.org/images/steps/developer/step-14.point-value.error-comparison.png" alt="">
4185
4186
4187
4188Checking these numbers, we see that for global refinement, the error
4189is proportional to @f$O(1/(sqrt(N) log(N)))@f$, and for the dual
4190estimator @f$O(1/N)@f$. Generally speaking, we see that the dual
4191weighted error estimator is better than the other refinement
4192indicators, at least when compared with those that have a similarly
4193regular behavior. The Kelly indicator produces smaller errors, but
4194jumps about the picture rather irregularly, with the error also
4195changing signs sometimes. Therefore, its behavior does not allow to
4196extrapolate the results to larger values of N. Furthermore, if we
4197trust the error estimates of the dual weighted error estimator, the
4198results can be improved by adding the estimated error to the computed
4199values. In terms of reliability, the weighted estimator is thus better
4200than the Kelly indicator, although the latter sometimes produces
4201smaller errors.
4202
4203
4204
4205<a name="Evaluationofpointstresses"></a><h3>Evaluation of point stresses</h3>
4206
4207
4208
4209Besides evaluating the values of the solution at a certain point, the
4210program also offers the possibility to evaluate the x-derivatives at a
4211certain point, and also to tailor mesh refinement for this. To let the
4212program compute these quantities, simply replace the two occurrences of
4213<code>PointValueEvaluation</code> in the main function by
4214<code>PointXDerivativeEvaluation</code>, and let the program run:
4215@code
4216Refinement cycle: 0
4217 Number of degrees of freedom=72
4218 Point x-derivative=-0.0719397
4219 Estimated error=-0.0126173
4220Refinement cycle: 1
4221 Number of degrees of freedom=61
4222 Point x-derivative=-0.0707956
4223 Estimated error=-0.00774316
4224Refinement cycle: 2
4225 Number of degrees of freedom=131
4226 Point x-derivative=-0.0568671
4227 Estimated error=-0.00313426
4228Refinement cycle: 3
4229 Number of degrees of freedom=247
4230 Point x-derivative=-0.053033
4231 Estimated error=-0.00136114
4232Refinement cycle: 4
4233 Number of degrees of freedom=532
4234 Point x-derivative=-0.0526429
4235 Estimated error=-0.000558868
4236Refinement cycle: 5
4237 Number of degrees of freedom=1267
4238 Point x-derivative=-0.0526955
4239 Estimated error=-0.000220116
4240Refinement cycle: 6
4241 Number of degrees of freedom=2864
4242 Point x-derivative=-0.0527495
4243 Estimated error=-9.46731e-05
4244Refinement cycle: 7
4245 Number of degrees of freedom=6409
4246 Point x-derivative=-0.052785
4247 Estimated error=-4.21543e-05
4248Refinement cycle: 8
4249 Number of degrees of freedom=14183
4250 Point x-derivative=-0.0528028
4251 Estimated error=-2.04241e-05
4252Refinement cycle: 9
4253 Number of degrees of freedom=29902
4254 Point x-derivative=-0.052814
4255@endcode
4256
4257
4258
4259The solution looks roughly the same as before (the exact solution of
4260course <em>is</em> the same, only the grid changed a little), but the
4261dual solution is now different. A close-up around the point of
4262evaluation shows this:
4263<table align="center">
4264 <tr>
4265 <td width="50%">
4266 <img src="https://www.dealii.org/images/steps/developer/step-14.point-derivative.solution-7-dual.png" alt="">
4267 </td>
4268 <td width="50%">
4269 <img src="https://www.dealii.org/images/steps/developer/step-14.point-derivative.solution-7-dual-close-up.png" alt="">
4270 </td>
4271</table>
4272This time, the grids in refinement cycles 0, 5, 6, 7, 8, and 9 look
4273like this:
4274
4275<table align="center" width="80%">
4276 <tr>
4277 <td><img src="https://www.dealii.org/images/steps/developer/step-14.point-derivative.grid-0.9.2.png" alt="" width="100%"></td>
4278 <td><img src="https://www.dealii.org/images/steps/developer/step-14.point-derivative.grid-5.9.2.png" alt="" width="100%"></td>
4279 <td><img src="https://www.dealii.org/images/steps/developer/step-14.point-derivative.grid-6.9.2.png" alt="" width="100%"></td>
4280 </tr>
4281 <tr>
4282 <td><img src="https://www.dealii.org/images/steps/developer/step-14.point-derivative.grid-7.9.2.png" alt="" width="100%"></td>
4283 <td><img src="https://www.dealii.org/images/steps/developer/step-14.point-derivative.grid-8.9.2.png" alt="" width="100%"></td>
4284 <td><img src="https://www.dealii.org/images/steps/developer/step-14.point-derivative.grid-9.9.2.png" alt="" width="100%"></td>
4285 </tr>
4286</table>
4287
4288Note the asymmetry of the grids compared with those we obtained for
4289the point evaluation. This is due to the fact that the domain and the primal
4290solution may be symmetric about the diagonal, but the @f$x@f$-derivative is
4291not, and the latter enters the refinement criterion.
4292
4293
4294
4295Then, it is interesting to compare actually computed values of the
4296quantity of interest (i.e. the x-derivative of the solution at one
4297point) with a reference value of -0.0528223... plus or minus
42980.0000005. We get this reference value by computing on finer grid after
4299some more mesh refinements, with approximately 130,000 cells.
4300Recall that if the error is @f$O(1/N)@f$ in the optimal case, then
4301taking a mesh with ten times more cells gives us one additional digit
4302in the result.
4303
4304
4305
4306In the left part of the following chart, you again see the convergence
4307of the error towards this extrapolated value, while on the right you
4308see a comparison of true and estimated error:
4309
4310<table width="80%" align="center">
4311 <tr>
4312 <td><img src="https://www.dealii.org/images/steps/developer/step-14.point-derivative.error.png" alt="" width="100%"></td>
4313 <td><img src="https://www.dealii.org/images/steps/developer/step-14.point-derivative.error-estimation.png" alt="" width="100%"></td>
4314 </tr>
4315</table>
4316
4317After an initial phase where the true error changes its sign, the
4318estimated error matches it quite well, again. Also note the dramatic
4319improvement in the error when using the estimated error to correct the
4320computed value of @f$J(u_h)@f$.
4321
4322
4323
4324<a name="step13revisited"></a><h3>step-13 revisited</h3>
4325
4326
4327
4328If instead of the <code>Exercise_2_3</code> data set, we choose
4329<code>CurvedRidges</code> in the main function, and choose @f$(0.5,0.5)@f$
4330as the evaluation point, then we can redo the
4331computations of the previous example program, to compare whether the
4332results obtained with the help of the dual weighted error estimator
4333are better than those we had previously.
4334
4335
4336
4337First, the meshes after 9 adaptive refinement cycles obtained with
4338the point evaluation and derivative evaluation refinement
4339criteria, respectively, look like this:
4340
4341<table width="80%" align="center">
4342 <tr>
4343 <td><img src="https://www.dealii.org/images/steps/developer/step-14.step-13.point-value.png" alt="" width="100%"></td>
4344 <td><img src="https://www.dealii.org/images/steps/developer/step-14.step-13.point-derivative.png" alt="" width="100%"></td>
4345 </tr>
4346</table>
4347
4348The features of the solution can still be seen in the mesh, but since the
4349solution is smooth, the singularities of the dual solution entirely
4350dominate the mesh refinement criterion, and lead to strongly
4351concentrated meshes. The solution after the seventh refinement step looks
4352like the following:
4353
4354<table width="40%" align="center">
4355 <tr>
4356 <td><img src="https://www.dealii.org/images/steps/developer/step-14.step-13.solution-7.9.2.png" alt="" width="100%"></td>
4357 </tr>
4358</table>
4359
4360Obviously, the solution is worse at some places, but the mesh
4361refinement process should have taken care that these places are not
4362important for computing the point value.
4363
4364
4365
4366
4367The next point is to compare the new (duality based) mesh refinement
4368criterion with the old ones. These are the results:
4369
4370<img src="https://www.dealii.org/images/steps/developer/step-14.step-13.error-comparison.png" alt="">
4371
4372
4373
4374The results are, well, somewhat mixed. First, the Kelly indicator
4375disqualifies itself by its unsteady behavior, changing the sign of the
4376error several times, and with increasing errors under mesh
4377refinement. The dual weighted error estimator has a monotone decrease
4378in the error, and is better than the weighted Kelly and global
4379refinement, but the margin is not as large as expected. This is, here,
4380due to the fact the global refinement can exploit the regular
4381structure of the meshes around the point of evaluation, which leads to
4382a better order of convergence for the point error. However, if we had
4383a mesh that is not locally rectangular, for example because we had to
4384approximate curved boundaries, or if the coefficients were not
4385constant, then this advantage of globally refinement meshes would
4386vanish, while the good performance of the duality based estimator
4387would remain.
4388
4389
4390
4391
4392<a name="Conclusionsandoutlook"></a><h3>Conclusions and outlook</h3>
4393
4394
4395
4396The results here are not too clearly indicating the superiority of the
4397dual weighted error estimation approach for mesh refinement over other
4398mesh refinement criteria, such as the Kelly indicator. This is due to
4399the relative simplicity of the shown applications. If you are not
4400convinced yet that this approach is indeed superior, you are invited
4401to browse through the literature indicated in the introduction, where
4402plenty of examples are provided where the dual weighted approach can
4403reduce the necessary numerical work by orders of magnitude, making
4404this the only way to compute certain quantities to reasonable
4405accuracies at all.
4406
4407
4408
4409Besides the objections you may raise against its use as a mesh
4410refinement criterion, consider that accurate knowledge of the error in
4411the quantity one might want to compute is of great use, since we can
4412stop computations when we are satisfied with the accuracy. Using more
4413traditional approaches, it is very difficult to get accurate estimates
4414for arbitrary quantities, except for, maybe, the error in the energy
4415norm, and we will then have no guarantee that the result we computed
4416satisfies any requirements on its accuracy. Also, as was shown for the
4417evaluation of point values and derivatives, the error estimate can be
4418used to extrapolate the results, yielding much higher accuracy in the
4419quantity we want to know.
4420
4421
4422
4423Leaving these mathematical considerations, we tried to write the
4424program in a modular way, such that implementing another test case, or
4425another evaluation and dual functional is simple. You are encouraged
4426to take the program as a basis for your own experiments, and to play a
4427little.
4428 *
4429 *
4430<a name="PlainProg"></a>
4431<h1> The plain program</h1>
4432@include "step-14.cc"
4433*/
void attach_dof_handler(const DoFHandler< dim, spacedim > &)
types::global_dof_index n_dofs() const
Definition fe_q.h:551
void write_svg(const Triangulation< 2, 2 > &tria, std::ostream &out) const
Definition grid_out.cc:1701
static void estimate(const Mapping< dim, spacedim > &mapping, const DoFHandler< dim, spacedim > &dof, const Quadrature< dim - 1 > &quadrature, const std::map< types::boundary_id, const Function< spacedim, typename InputVector::value_type > * > &neumann_bc, const InputVector &solution, Vector< float > &error, const ComponentMask &component_mask=ComponentMask(), const Function< spacedim > *coefficients=nullptr, const unsigned int n_threads=numbers::invalid_unsigned_int, const types::subdomain_id subdomain_id=numbers::invalid_subdomain_id, const types::material_id material_id=numbers::invalid_material_id, const Strategy strategy=cell_diameter_over_24)
Definition point.h:112
void initialize(const MatrixType &A, const AdditionalData &parameters=AdditionalData())
Point< 3 > vertices[4]
Point< 2 > second
Definition grid_out.cc:4616
Point< 2 > first
Definition grid_out.cc:4615
unsigned int level
Definition grid_out.cc:4618
__global__ void set(Number *val, const Number s, const size_type N)
#define Assert(cond, exc)
#define AssertThrow(cond, exc)
typename ActiveSelector::cell_iterator cell_iterator
typename ActiveSelector::face_iterator face_iterator
typename ActiveSelector::active_cell_iterator active_cell_iterator
void loop(ITERATOR begin, std_cxx20::type_identity_t< ITERATOR > end, DOFINFO &dinfo, INFOBOX &info, const std::function< void(DOFINFO &, typename INFOBOX::CellInfo &)> &cell_worker, const std::function< void(DOFINFO &, typename INFOBOX::CellInfo &)> &boundary_worker, const std::function< void(DOFINFO &, DOFINFO &, typename INFOBOX::CellInfo &, typename INFOBOX::CellInfo &)> &face_worker, ASSEMBLER &assembler, const LoopControl &lctrl=LoopControl())
Definition loop.h:439
void make_hanging_node_constraints(const DoFHandler< dim, spacedim > &dof_handler, AffineConstraints< number > &constraints)
void make_sparsity_pattern(const DoFHandler< dim, spacedim > &dof_handler, SparsityPatternBase &sparsity_pattern, const AffineConstraints< number > &constraints=AffineConstraints< number >(), const bool keep_constrained_dofs=true, const types::subdomain_id subdomain_id=numbers::invalid_subdomain_id)
@ update_hessians
Second derivatives of shape functions.
@ update_values
Shape function values.
@ update_normal_vectors
Normal vectors.
@ update_JxW_values
Transformed quadrature weights.
@ update_gradients
Shape function gradients.
@ update_quadrature_points
Transformed quadrature points.
Task< RT > new_task(const std::function< RT()> &function)
const Event initial
Definition event.cc:65
void apply(const Kokkos::TeamPolicy< MemorySpace::Default::kokkos_space::execution_space >::member_type &team_member, const Kokkos::View< Number *, MemorySpace::Default::kokkos_space > shape_data, const ViewTypeIn in, ViewTypeOut out)
Expression fabs(const Expression &x)
Expression sign(const Expression &x)
void interpolation_difference(const DoFHandler< dim, spacedim > &dof1, const InVector &z1, const FiniteElement< dim, spacedim > &fe2, OutVector &z1_difference)
void interpolate(const DoFHandler< dim, spacedim > &dof1, const InVector &u1, const DoFHandler< dim, spacedim > &dof2, OutVector &u2)
void refine(Triangulation< dim, spacedim > &tria, const Vector< Number > &criteria, const double threshold, const unsigned int max_to_mark=numbers::invalid_unsigned_int)
void refine_and_coarsen_fixed_number(Triangulation< dim, spacedim > &triangulation, const Vector< Number > &criteria, const double top_fraction_of_cells, const double bottom_fraction_of_cells, const unsigned int max_n_cells=std::numeric_limits< unsigned int >::max())
void coarsen(Triangulation< dim, spacedim > &tria, const Vector< Number > &criteria, const double threshold)
void refine_and_coarsen_fixed_fraction(Triangulation< dim, spacedim > &tria, const Vector< Number > &criteria, const double top_fraction, const double bottom_fraction, const unsigned int max_n_cells=std::numeric_limits< unsigned int >::max(), const VectorTools::NormType norm_type=VectorTools::NormType::L1_norm)
@ valid
Iterator points to a valid object.
@ matrix
Contents is actually a matrix.
void cell_matrix(FullMatrix< double > &M, const FEValuesBase< dim > &fe, const FEValuesBase< dim > &fetest, const ArrayView< const std::vector< double > > &velocity, const double factor=1.)
Definition advection.h:75
void cell_residual(Vector< double > &result, const FEValuesBase< dim > &fe, const std::vector< Tensor< 1, dim > > &input, const ArrayView< const std::vector< double > > &velocity, double factor=1.)
Definition advection.h:131
void apply_boundary_values(const std::map< types::global_dof_index, number > &boundary_values, SparseMatrix< number > &matrix, Vector< number > &solution, Vector< number > &right_hand_side, const bool eliminate_columns=true)
Point< spacedim > point(const gp_Pnt &p, const double tolerance=1e-10)
Definition utilities.cc:189
SymmetricTensor< 2, dim, Number > C(const Tensor< 2, dim, Number > &F)
SymmetricTensor< 2, dim, Number > d(const Tensor< 2, dim, Number > &F, const Tensor< 2, dim, Number > &dF_dt)
constexpr ReturnType< rank, T >::value_type & extract(T &t, const ArrayType &indices)
void call(const std::function< RT()> &function, internal::return_value< RT > &ret_val)
VectorType::value_type * end(VectorType &V)
T sum(const T &t, const MPI_Comm mpi_communicator)
void interpolate_boundary_values(const Mapping< dim, spacedim > &mapping, const DoFHandler< dim, spacedim > &dof, const std::map< types::boundary_id, const Function< spacedim, number > * > &function_map, std::map< types::global_dof_index, number > &boundary_values, const ComponentMask &component_mask=ComponentMask())
void run(const Iterator &begin, const std_cxx20::type_identity_t< Iterator > &end, Worker worker, Copier copier, const ScratchData &sample_scratch_data, const CopyData &sample_copy_data, const unsigned int queue_length, const unsigned int chunk_size)
void run(const std::vector< std::vector< Iterator > > &colored_iterators, Worker worker, Copier copier, const ScratchData &sample_scratch_data, const CopyData &sample_copy_data, const unsigned int queue_length=2 *MultithreadInfo::n_threads(), const unsigned int chunk_size=8)
bool check(const ConstraintKinds kind_in, const unsigned int dim)
void copy(const T *begin, const T *end, U *dest)
int(&) functions(const void *v1, const void *v2)
static const unsigned int invalid_unsigned_int
Definition types.h:213
STL namespace.
Definition types.h:33
const ::parallel::distributed::Triangulation< dim, spacedim > * triangulation
const TriangulationDescription::Settings settings