Reference documentation for deal.II version Git d234d5a459 2020-07-13 15:04:13 -0400
TensorFunction< rank, dim, Number > Class Template Reference

#include <deal.II/base/tensor_function.h>

Inheritance diagram for TensorFunction< rank, dim, Number >:
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## Public Types

using value_type = Tensor< rank, dim, Number >

using gradient_type = Tensor< rank+1, dim, Number >

using time_type = typename FunctionTime< typename numbers::NumberTraits< Number >::real_type >::time_type

Public Types inherited from FunctionTime< numbers::NumberTraits< Number >::real_type >
using time_type = numbers::NumberTraits< Number >::real_type

## Public Member Functions

TensorFunction (const time_type initial_time=time_type(0.0))

virtual ~TensorFunction () override=default

virtual value_type value (const Point< dim > &p) const

virtual void value_list (const std::vector< Point< dim >> &points, std::vector< value_type > &values) const

Public Member Functions inherited from FunctionTime< numbers::NumberTraits< Number >::real_type >
FunctionTime (const numbers::NumberTraits< Number >::real_type initial_time=numbers::NumberTraits< Number >::real_type(0.0))

virtual ~FunctionTime ()=default

numbers::NumberTraits< Number >::real_type get_time () const

virtual void set_time (const numbers::NumberTraits< Number >::real_type new_time)

virtual void advance_time (const numbers::NumberTraits< Number >::real_type delta_t)

Public Member Functions inherited from Subscriptor
Subscriptor ()

Subscriptor (const Subscriptor &)

Subscriptor (Subscriptor &&) noexcept

virtual ~Subscriptor ()

Subscriptoroperator= (const Subscriptor &)

Subscriptoroperator= (Subscriptor &&) noexcept

void subscribe (std::atomic< bool > *const validity, const std::string &identifier="") const

void unsubscribe (std::atomic< bool > *const validity, const std::string &identifier="") const

unsigned int n_subscriptions () const

template<typename StreamType >
void list_subscribers (StreamType &stream) const

void list_subscribers () const

template<class Archive >
void serialize (Archive &ar, const unsigned int version)

Static Public Member Functions inherited from Subscriptor
static ::ExceptionBaseExcInUse (int arg1, std::string arg2, std::string arg3)

static ::ExceptionBaseExcNoSubscriber (std::string arg1, std::string arg2)

## Detailed Description

### template<int rank, int dim, typename Number = double> class TensorFunction< rank, dim, Number >

This class is a model for a tensor valued function. The interface of the class is mostly the same as that for the Function class, with the exception that it does not support vector-valued functions with several components, but that the return type is always tensor-valued. The returned values of the evaluation of objects of this type are always whole tensors, while for the Function class, one can ask for a specific component only, or use the vector_value function, which however does not return the value, but rather writes it into the address provided by its second argument. The reason for the different behaviour of the classes is that in the case of tensor valued functions, the size of the argument is known to the compiler a priori, such that the correct amount of memory can be allocated on the stack for the return value; on the other hand, for the vector valued functions, the size is not known to the compiler, so memory has to be allocated on the heap, resulting in relatively expensive copy operations. One can therefore consider this class a specialization of the Function class for which the size is known. An additional benefit is that tensors of arbitrary rank can be returned, not only vectors, as for them the size can be determined similarly simply.

Definition at line 56 of file tensor_function.h.

## ◆ value_type

template<int rank, int dim, typename Number = double>
 using TensorFunction< rank, dim, Number >::value_type = Tensor

Alias for the return types of the value function.

Definition at line 64 of file tensor_function.h.

template<int rank, int dim, typename Number = double>
 using TensorFunction< rank, dim, Number >::gradient_type = Tensor

Alias for the return types of the gradient functions.

Definition at line 69 of file tensor_function.h.

## ◆ time_type

template<int rank, int dim, typename Number = double>
 using TensorFunction< rank, dim, Number >::time_type = typename FunctionTime< typename numbers::NumberTraits::real_type>::time_type

The scalar-valued real type used for representing time.

Definition at line 75 of file tensor_function.h.

## ◆ TensorFunction()

template<int rank, int dim, typename Number = double>
 TensorFunction< rank, dim, Number >::TensorFunction ( const time_type initial_time = time_type(0.0) )

Constructor. May take an initial value for the time variable, which defaults to zero.

## ◆ ~TensorFunction()

template<int rank, int dim, typename Number = double>
 virtual TensorFunction< rank, dim, Number >::~TensorFunction ( )
overridevirtualdefault

Virtual destructor; absolutely necessary in this case, as classes are usually not used by their true type, but rather through pointers to this base class.

## ◆ value()

template<int rank, int dim, typename Number = double>
 virtual value_type TensorFunction< rank, dim, Number >::value ( const Point< dim > & p ) const
virtual

Return the value of the function at the given point.

Reimplemented in TensorFunctionParser< rank, dim, Number >, and ConstantTensorFunction< rank, dim, Number >.

## ◆ value_list()

template<int rank, int dim, typename Number = double>
 virtual void TensorFunction< rank, dim, Number >::value_list ( const std::vector< Point< dim >> & points, std::vector< value_type > & values ) const
virtual

Set values to the point values of the function at the points. It is assumed that values already has the right size, i.e. the same size as the points array.

template<int rank, int dim, typename Number = double>
 virtual gradient_type TensorFunction< rank, dim, Number >::gradient ( const Point< dim > & p ) const
virtual

Return the gradient of the function at the given point.

Reimplemented in ConstantTensorFunction< rank, dim, Number >.

Set gradients to the gradients of the function at the points. It is assumed that values already has the right size, i.e. the same size as the points array.