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| | LowStorageRungeKutta ()=default |
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| | LowStorageRungeKutta (const runge_kutta_method method) |
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| void | initialize (const runge_kutta_method method) override |
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| double | evolve_one_time_step (const std::function< VectorType(const double, const VectorType &)> &f, const std::function< VectorType(const double, const double, const VectorType &)> &id_minus_tau_J_inverse, double t, double delta_t, VectorType &y) override |
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| double | evolve_one_time_step (const std::function< VectorType(const double, const VectorType &)> &f, double t, double delta_t, VectorType &solution, VectorType &vec_ri, VectorType &vec_ki) |
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| void | get_coefficients (std::vector< double > &a, std::vector< double > &b, std::vector< double > &c) const |
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| const Status & | get_status () const override |
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| double | evolve_one_time_step (std::vector< std::function< VectorType(const double, const VectorType &)> > &F, std::vector< std::function< VectorType(const double, const double, const VectorType &)> > &J_inverse, double t, double delta_t, VectorType &y) override |
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template<typename VectorType>
class TimeStepping::LowStorageRungeKutta< VectorType >
The LowStorageRungeKutta class is derived from RungeKutta and implements a specific class of explicit methods. The main advantages of low-storage methods are the reduced memory consumption and the reduced memory access.
Definition at line 410 of file time_stepping.h.
template<typename VectorType >
| double TimeStepping::LowStorageRungeKutta< VectorType >::evolve_one_time_step |
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const std::function< VectorType(const double, const VectorType &)> & | f, |
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const std::function< VectorType(const double, const double, const VectorType &)> & | id_minus_tau_J_inverse, |
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double | t, |
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double | delta_t, |
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VectorType & | y ) |
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overridevirtual |
This function is used to advance from time t to t+ delta_t. f is the function \( f(t,y) \) that should be integrated, the input parameters are the time t and the vector y and the output is value of f at this point. id_minus_tau_J_inverse is a function that computes \(
inv(I-\tau J)\) where \( I \) is the identity matrix, \( \tau \) is given, and \( J \) is the Jacobian \( \frac{\partial f}{\partial y} \). The input parameters are the time, \( \tau \), and a vector. The output is the value of function at this point. evolve_one_time_step returns the time at the end of the time step.
Implements TimeStepping::RungeKutta< VectorType >.
template<typename VectorType >
| double TimeStepping::RungeKutta< VectorType >::evolve_one_time_step |
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std::vector< std::function< VectorType(const double, const VectorType &)> > & | F, |
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std::vector< std::function< VectorType(const double, const double, const VectorType &)> > & | J_inverse, |
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double | t, |
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double | delta_t, |
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VectorType & | y ) |
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overridevirtualinherited |
This function is used to advance from time t to t+ delta_t. F is a vector of functions \( f(t,y) \) that should be integrated, the input parameters are the time t and the vector y and the output is value of f at this point. J_inverse is a vector functions that compute the inverse of the Jacobians associated to the implicit problems. The input parameters are the time, \( \tau \), and a vector. The output is the value of function at this point. This function returns the time at the end of the time step. When using Runge-Kutta methods, F and J_inverse can only contain one element.
Implements TimeStepping::TimeStepping< VectorType >.
1.11.0