#include <deal.II/differentiation/ad/ad_drivers.h>

Static Public Member Functions
Configuration
static void	initialize_global_environment (const unsigned int n_independent_variables)

Drivers for scalar functions
static ScalarType	value (const std::vector< ADNumberType > &dependent_variables)

static void	gradient (const std::vector< ADNumberType > &independent_variables, const std::vector< ADNumberType > &dependent_variables, Vector< ScalarType > &gradient)

static void	hessian (const std::vector< ADNumberType > &independent_variables, const std::vector< ADNumberType > &dependent_variables, FullMatrix< ScalarType > &hessian)

Drivers for vector functions
static void	values (const std::vector< ADNumberType > &dependent_variables, Vector< ScalarType > &values)

static void	jacobian (const std::vector< ADNumberType > &independent_variables, const std::vector< ADNumberType > &dependent_variables, FullMatrix< ScalarType > &jacobian)

Detailed Description

template<typename ADNumberType, typename ScalarType, typename T = void>
struct Differentiation::AD::TapelessDrivers< ADNumberType, ScalarType, T >

A prototype driver class for tapeless auto-differentiable numbers.

It is intended that this class be specialized for the valid combinations of auto-differentiable numbers and output scalar number types.

Template Parameters

ADNumberType	A type corresponding to a supported auto-differentiable number.
ScalarType	A real or complex floating point number type that is the scalar value type used for input to, and output from, operations performed with auto-differentiable numbers.
T	An arbitrary type resulting from the application of the SFINAE idiom to selectively specialize this class.

Author: Jean-Paul Pelteret, 2017

Definition at line 352 of file ad_drivers.h.

Member Function Documentation

template<typename ADNumberType , typename ScalarType , typename T = void>

static void Differentiation::AD::TapelessDrivers< ADNumberType, ScalarType, T >::initialize_global_environment ( const unsigned int n_independent_variables )

static

In the event that the tapeless mode requires a priori knowledge of how many directional derivatives might need to be computed, this function informs the auto-differention library of what this number is.

Parameters

[in] n_independent_variables The number of independent variables that will be used for the entire duration of the simulation.

Warning: For ADOL-C tapeless numbers, the value given to n_independent_variables should be the maximum number of independent variables that will be used for the entire duration of the simulation. This is important in the context of, for example, hp-FEM and for multiple constitutive models with a different number of fields from which a linearization must be computed.

template<typename ADNumberType , typename ScalarType , typename T = void>

static ScalarType Differentiation::AD::TapelessDrivers< ADNumberType, ScalarType, T >::value ( const std::vector< ADNumberType > & dependent_variables )

static

Computes the value of the scalar field.

Parameters

[in] dependent_variables The dependent variables whose values are to be extracted.

Returns: The scalar value of the function.

template<typename ADNumberType , typename ScalarType , typename T = void>

static void Differentiation::AD::TapelessDrivers< ADNumberType, ScalarType, T >::gradient	(	const std::vector< ADNumberType > &	independent_variables,
		const std::vector< ADNumberType > &	dependent_variables,
		Vector< ScalarType > &	gradient
	)

static

Computes the gradient of the scalar field with respect to all independent variables.

Parameters

[in]	independent_variables	The independent variables whose sensitivities were tracked.
[in]	dependent_variables	The (single) dependent variable whose gradients are to be extracted.
[out]	gradient	The scalar values of the dependent function's gradients. It is expected that this vector be of the correct size (with length `n_independent_variables`).

template<typename ADNumberType , typename ScalarType , typename T = void>

static void Differentiation::AD::TapelessDrivers< ADNumberType, ScalarType, T >::hessian	(	const std::vector< ADNumberType > &	independent_variables,
		const std::vector< ADNumberType > &	dependent_variables,
		FullMatrix< ScalarType > &	hessian
	)

static

Computes the Hessian of the scalar field with respect to all independent variables.

Parameters

[in]	independent_variables	The independent variables whose sensitivities were tracked.
[in]	dependent_variables	The (single) dependent variable whose Hessians are to be extracted.
[out]	hessian	The scalar values of the dependent function's Hessian. It is expected that this matrix be of the correct size (with dimensions `n_independent_variables` \(\times\)`n_independent_variables`).

template<typename ADNumberType , typename ScalarType , typename T = void>

static void Differentiation::AD::TapelessDrivers< ADNumberType, ScalarType, T >::values	(	const std::vector< ADNumberType > &	dependent_variables,
		Vector< ScalarType > &	values
	)

static

Computes the values of the vector field.

Parameters

[in]	dependent_variables	The dependent variables whose Hessians are to be extracted.
[out]	values	The scalar values of the dependent functions. It is expected that this vector be of the correct size (with length `n_dependent_variables`).

template<typename ADNumberType , typename ScalarType , typename T = void>

static void Differentiation::AD::TapelessDrivers< ADNumberType, ScalarType, T >::jacobian	(	const std::vector< ADNumberType > &	independent_variables,
		const std::vector< ADNumberType > &	dependent_variables,
		FullMatrix< ScalarType > &	jacobian
	)

static

Computes the Jacobian of the vector field.

The Jacobian of a vector field is in essense the gradient of each dependent variable with respect to all independent variables. This operation is therefore analogous to the gradient() operation performed on a collection of scalar valued fields.

Parameters

[in]	independent_variables	The independent variables whose sensitivities were tracked.
[in]	dependent_variables	The dependent variables whose Jacobian are to be extracted.
[out]	jacobian	The scalar values of the dependent functions' Jacobian. It is expected that this matrix be of the correct size (with dimensions `n_dependent_variables` \(\times\)`n_independent_variables`).

The documentation for this struct was generated from the following file:

deal.II/differentiation/ad/ad_drivers.h

Static Public Member Functions

Detailed Description

template<typename ADNumberType, typename ScalarType, typename T = void> struct Differentiation::AD::TapelessDrivers< ADNumberType, ScalarType, T >

Member Function Documentation

template<typename ADNumberType, typename ScalarType, typename T = void>
struct Differentiation::AD::TapelessDrivers< ADNumberType, ScalarType, T >