#include <LibtorchANNSurrogate.h>

Inheritance diagram for LibtorchANNSurrogate:

Public Types
typedef DataFileName	DataFileParameterType

Public Member Functions
	LibtorchANNSurrogate (const InputParameters &parameters)

virtual Real	evaluate (const std::vector< Real > &x) const override
	Evaluate surrogate model given a row of parameters. More...

virtual Real	evaluate (const std::vector< Real > &x) const
	Evaluate surrogate model given a row of parameters. More...

virtual void	evaluate (const std::vector< Real > &x, std::vector< Real > &y) const
	Various evaluate methods that can be overriden. More...

virtual Real	evaluate (const std::vector< Real > &x, Real &std) const
	Evaluate methods that also return predicted standard deviation (see GaussianProcess.h) More...

virtual void	evaluate (const std::vector< Real > &x, std::vector< Real > &y, std::vector< Real > &) const

virtual bool	enabled () const

std::shared_ptr< MooseObject >	getSharedPtr ()

std::shared_ptr< const MooseObject >	getSharedPtr () const

MooseApp &	getMooseApp () const

const std::string &	type () const

virtual const std::string &	name () const

std::string	typeAndName () const

std::string	errorPrefix (const std::string &error_type) const

void	callMooseError (std::string msg, const bool with_prefix) const

MooseObjectParameterName	uniqueParameterName (const std::string &parameter_name) const

const InputParameters &	parameters () const

MooseObjectName	uniqueName () const

const T &	getParam (const std::string &name) const

std::vector< std::pair< T1, T2 > >	getParam (const std::string &param1, const std::string &param2) const

const T *	queryParam (const std::string &name) const

const T &	getRenamedParam (const std::string &old_name, const std::string &new_name) const

T	getCheckedPointerParam (const std::string &name, const std::string &error_string="") const

bool	isParamValid (const std::string &name) const

bool	isParamSetByUser (const std::string &nm) const

void	paramError (const std::string &param, Args... args) const

void	paramWarning (const std::string &param, Args... args) const

void	paramInfo (const std::string &param, Args... args) const

void	connectControllableParams (const std::string &parameter, const std::string &object_type, const std::string &object_name, const std::string &object_parameter) const

void	mooseError (Args &&... args) const

void	mooseErrorNonPrefixed (Args &&... args) const

void	mooseDocumentedError (const std::string &repo_name, const unsigned int issue_num, Args &&... args) const

void	mooseWarning (Args &&... args) const

void	mooseWarningNonPrefixed (Args &&... args) const

void	mooseDeprecated (Args &&... args) const

void	mooseInfo (Args &&... args) const

std::string	getDataFileName (const std::string &param) const

std::string	getDataFileNameByName (const std::string &relative_path) const

std::string	getDataFilePath (const std::string &relative_path) const

const Parallel::Communicator &	comm () const

processor_id_type	n_processors () const

processor_id_type	processor_id () const

T &	getSampler (const std::string &name)

Sampler &	getSampler (const std::string &name)

T &	getSamplerByName (const SamplerName &name)

Sampler &	getSamplerByName (const SamplerName &name)

template<>
SurrogateModel &	getSurrogateModel (const std::string &name) const

template<>
SurrogateTrainerBase &	getSurrogateTrainer (const std::string &name) const

template<>
SurrogateModel &	getSurrogateModelByName (const UserObjectName &name) const

template<>
SurrogateTrainerBase &	getSurrogateTrainerByName (const UserObjectName &name) const

const std::string &	modelMetaDataName () const
	Accessor for the name of the model meta data. More...

const FileName &	getModelDataFileName () const
	Get the associated filename. More...

bool	hasModelData () const
	Check if we need to load model data (if the filename parameter is used) More...


virtual void	evaluate (const std::vector< Real > &x, std::vector< Real > &y) const
	Various evaluate methods that can be overriden. More...


virtual Real	evaluate (const std::vector< Real > &x, Real &std) const
	Evaluate methods that also return predicted standard deviation (see GaussianProcess.h) More...

virtual void	evaluate (const std::vector< Real > &x, std::vector< Real > &y, std::vector< Real > &) const


template<typename T = SurrogateModel>
T &	getSurrogateModel (const std::string &name) const
	Get a SurrogateModel/Trainer with a given name. More...

template<typename T = SurrogateTrainerBase>
T &	getSurrogateTrainer (const std::string &name) const


template<typename T = SurrogateModel>
T &	getSurrogateModelByName (const UserObjectName &name) const
	Get a sampler with a given name. More...

template<typename T = SurrogateTrainerBase>
T &	getSurrogateTrainerByName (const UserObjectName &name) const


template<typename T , typename... Args>
T &	declareModelData (const std::string &data_name, Args &&... args)
	Declare model data for loading from file as well as restart. More...


template<typename T , typename... Args>
const T &	getModelData (const std::string &data_name, Args &&... args) const
	Retrieve model data from the interface. More...

Static Public Member Functions
static InputParameters	validParams ()

static MooseEnum	defaultPredictorTypes ()

static MooseEnum	defaultResponseTypes ()

Public Attributes
const ConsoleStream	_console

Protected Attributes
const std::shared_ptr< Moose::LibtorchArtificialNeuralNet > &	_nn
	Pointer to the neural net object (initialized as null) More...

const StochasticTools::Standardizer &	_input_standardizer
	Standardizer for use with input (x) More...

const StochasticTools::Standardizer &	_output_standardizer
	Standardizer for use with output response (y) More...

const bool &	_enabled

MooseApp &	_app

const std::string	_type

const std::string	_name

const InputParameters &	_pars

Factory &	_factory

ActionFactory &	_action_factory

const Parallel::Communicator &	_communicator

Detailed Description

Definition at line 19 of file LibtorchANNSurrogate.h.

Constructor & Destructor Documentation

◆ LibtorchANNSurrogate()

LibtorchANNSurrogate::LibtorchANNSurrogate ( const InputParameters & parameters )

Definition at line 24 of file LibtorchANNSurrogate.C.

   : SurrogateModel(parameters),
     _nn(getModelData<std::shared_ptr<Moose::LibtorchArtificialNeuralNet>>("nn")),
     _input_standardizer(getModelData<StochasticTools::Standardizer>("input_standardizer")),
     _output_standardizer(getModelData<StochasticTools::Standardizer>("output_standardizer"))
 {
   // We check if MOOSE is compiled with torch, if not this throws an error
   StochasticToolsApp::requiresTorch(*this);
 }

Member Function Documentation

◆ declareModelData()

template<typename T , typename... Args>

T & RestartableModelInterface::declareModelData	(	const std::string &	data_name,
		Args &&...	args
	)

inherited

Declare model data for loading from file as well as restart.

Definition at line 78 of file RestartableModelInterface.h.

 {
   return _model_restartable.declareRestartableData<T>(data_name, std::forward<Args>(args)...);
 }

◆ defaultPredictorTypes()

static MooseEnum SurrogateModel::defaultPredictorTypes ( )

inlinestaticinherited

Definition at line 27 of file SurrogateModel.h.

27 { return MooseEnum("real"); }

MooseEnum

◆ defaultResponseTypes()

static MooseEnum SurrogateModel::defaultResponseTypes ( )

inlinestaticinherited

Definition at line 28 of file SurrogateModel.h.

Referenced by EvaluateSurrogate::validParams().

28 { return MooseEnum("real vector_real"); }

MooseEnum

◆ evaluate() [1/8]

virtual void SurrogateModel::evaluate

inline

Various evaluate methods that can be overriden.

Definition at line 43 of file SurrogateModel.h.

   {
     evaluateError(x, y);
   }

◆ evaluate() [2/8]

virtual Real SurrogateModel::evaluate

inline

Evaluate methods that also return predicted standard deviation (see GaussianProcess.h)

Definition at line 53 of file SurrogateModel.h.

   {
     evaluateError(x, std, true);
     return 0.0;
   }

◆ evaluate() [3/8]

virtual Real SurrogateModel::evaluate

inline

Evaluate surrogate model given a row of parameters.

Definition at line 33 of file SurrogateModel.h.

   {
     evaluateError(x, Real());
     return 0.0;
   };

◆ evaluate() [4/8]

virtual void SurrogateModel::evaluate

inline

Definition at line 59 of file SurrogateModel.h.

   {
     evaluateError(x, y, true);
   }

◆ evaluate() [5/8]

Real LibtorchANNSurrogate::evaluate ( const std::vector< Real > & x ) const

overridevirtual

Evaluate surrogate model given a row of parameters.

Reimplemented from SurrogateModel.

Definition at line 35 of file LibtorchANNSurrogate.C.

 {
   Real val(0.0);
 
   // Check whether input point has same dimensionality as training data
   mooseAssert(_nn->numInputs() == x.size(),
               "Input point does not match dimensionality of training data.");
 
   std::vector<Real> converted_input(x.size(), 0);
   const auto & input_mean = _input_standardizer.getMean();
   const auto & input_std = _input_standardizer.getStdDev();
 
   mooseAssert(input_mean.size() == converted_input.size() &&
                   input_std.size() == converted_input.size(),
               "The input standardizer's dimensions should be the same as the input dimension!");
 
   for (auto input_i : index_range(converted_input))
     converted_input[input_i] = (x[input_i] - input_mean[input_i]) / input_std[input_i];
 
   torch::Tensor x_tf =
       torch::tensor(torch::ArrayRef<Real>(converted_input.data(), converted_input.size()))
           .to(at::kDouble);
 
   const auto & output_mean = _output_standardizer.getMean();
   const auto & output_std = _output_standardizer.getStdDev();
 
   mooseAssert(output_mean.size() == 1 && output_std.size() == 1,
               "The output standardizer's dimensions should be 1!");
 
   // Compute prediction
   val = _nn->forward(x_tf).item<double>();
   val = val * output_std[0] + output_mean[0];
 
   return val;
 }

◆ evaluate() [6/8]

virtual void SurrogateModel::evaluate	(	const std::vector< Real > &	x,
		std::vector< Real > &	y
	)		const

inlinevirtualinherited

Various evaluate methods that can be overriden.

Reimplemented in GaussianProcessSurrogate, PolynomialRegressionSurrogate, and NearestPointSurrogate.

Definition at line 43 of file SurrogateModel.h.

   {
     evaluateError(x, y);
   }

◆ evaluate() [7/8]

virtual Real SurrogateModel::evaluate	(	const std::vector< Real > &	x,
		Real &	std
	)		const

inlinevirtualinherited

Evaluate methods that also return predicted standard deviation (see GaussianProcess.h)

Reimplemented in GaussianProcessSurrogate.

Definition at line 53 of file SurrogateModel.h.

   {
     evaluateError(x, std, true);
     return 0.0;
   }

◆ evaluate() [8/8]

virtual void SurrogateModel::evaluate	(	const std::vector< Real > &	x,
		std::vector< Real > &	y,
		std::vector< Real > &
	)		const

inlinevirtualinherited

Reimplemented in GaussianProcessSurrogate.

Definition at line 59 of file SurrogateModel.h.

   {
     evaluateError(x, y, true);
   }

◆ getModelData()

template<typename T , typename... Args>

const T & RestartableModelInterface::getModelData	(	const std::string &	data_name,
		Args &&...	args
	)		const

inherited

Retrieve model data from the interface.

Definition at line 85 of file RestartableModelInterface.h.

 {
   return _model_restartable.getRestartableData<T>(data_name, std::forward<Args>(args)...);
 }

◆ getModelDataFileName()

const FileName & RestartableModelInterface::getModelDataFileName ( ) const

inherited

Get the associated filename.

Definition at line 33 of file RestartableModelInterface.C.

 {
   return _model_object.getParam<FileName>("filename");
 }

◆ getSurrogateModel() [1/2]

template<>

SurrogateModel& SurrogateModelInterface::getSurrogateModel ( const std::string & name ) const

inherited

Definition at line 46 of file SurrogateModelInterface.C.

 {
   return getSurrogateModelByName<SurrogateModel>(_smi_params.get<UserObjectName>(name));
 }

◆ getSurrogateModel() [2/2]

template<typename T >

T & SurrogateModelInterface::getSurrogateModel ( const std::string & name ) const

inherited

Get a SurrogateModel/Trainer with a given name.

Parameters

name	The name of the parameter key of the sampler to retrieve

Returns: The sampler with name associated with the parameter 'name'

Definition at line 81 of file SurrogateModelInterface.h.

Referenced by SurrogateTrainer::initialize().

 {
   return getSurrogateModelByName<T>(_smi_params.get<UserObjectName>(name));
 }

◆ getSurrogateModelByName() [1/2]

template<>

SurrogateModel& SurrogateModelInterface::getSurrogateModelByName ( const UserObjectName & name ) const

inherited

Definition at line 31 of file SurrogateModelInterface.C.

 {
   std::vector<SurrogateModel *> models;
   _smi_feproblem.theWarehouse()
       .query()
       .condition<AttribName>(name)
       .condition<AttribSystem>("SurrogateModel")
       .queryInto(models);
   if (models.empty())
     mooseError("Unable to find a SurrogateModel object with the name '" + name + "'");
   return *(models[0]);
 }

◆ getSurrogateModelByName() [2/2]

template<typename T >

T & SurrogateModelInterface::getSurrogateModelByName ( const UserObjectName & name ) const

inherited

Get a sampler with a given name.

Parameters

name	The name of the sampler to retrieve

Returns: The sampler with name 'name'

Definition at line 88 of file SurrogateModelInterface.h.

Referenced by CrossValidationScores::CrossValidationScores(), EvaluateSurrogate::EvaluateSurrogate(), and InverseMapping::initialSetup().

 {
   std::vector<T *> models;
   _smi_feproblem.theWarehouse()
       .query()
       .condition<AttribName>(name)
       .condition<AttribSystem>("SurrogateModel")
       .queryInto(models);
   if (models.empty())
     mooseError("Unable to find a SurrogateModel object of type " + std::string(typeid(T).name()) +
                " with the name '" + name + "'");
   return *(models[0]);
 }

◆ getSurrogateTrainer() [1/2]

template<typename T >

T & SurrogateModelInterface::getSurrogateTrainer ( const std::string & name ) const

inherited

Definition at line 104 of file SurrogateModelInterface.h.

 {
   return getSurrogateTrainerByName<T>(_smi_params.get<UserObjectName>(name));
 }

◆ getSurrogateTrainer() [2/2]

template<>

SurrogateTrainerBase& SurrogateModelInterface::getSurrogateTrainer ( const std::string & name ) const

inherited

Definition at line 60 of file SurrogateModelInterface.C.

 {
   return getSurrogateTrainerByName<SurrogateTrainerBase>(_smi_params.get<UserObjectName>(name));
 }

◆ getSurrogateTrainerByName() [1/2]

template<>

SurrogateTrainerBase& SurrogateModelInterface::getSurrogateTrainerByName ( const UserObjectName & name ) const

inherited

Definition at line 53 of file SurrogateModelInterface.C.

 {
   return _smi_feproblem.getUserObject<SurrogateTrainerBase>(name);
 }

◆ getSurrogateTrainerByName() [2/2]

template<typename T >

T & SurrogateModelInterface::getSurrogateTrainerByName ( const UserObjectName & name ) const

inherited

Definition at line 111 of file SurrogateModelInterface.h.

Referenced by SurrogateTrainerOutput::output().

 {
   SurrogateTrainerBase * base_ptr =
       &_smi_feproblem.getUserObject<SurrogateTrainerBase>(name, _smi_tid);
   T * obj_ptr = dynamic_cast<T *>(base_ptr);
   if (!obj_ptr)
     mooseError("Failed to find a SurrogateTrainer object of type " + std::string(typeid(T).name()) +
                    " with the name '",
                name,
                "' for the desired type.");
   return *obj_ptr;
 }

◆ hasModelData()

bool RestartableModelInterface::hasModelData ( ) const

inherited

Check if we need to load model data (if the filename parameter is used)

Definition at line 39 of file RestartableModelInterface.C.

 {
   return _model_object.isParamValid("filename");
 }

◆ modelMetaDataName()

const std::string& RestartableModelInterface::modelMetaDataName ( ) const

inlineinherited

Accessor for the name of the model meta data.

Definition at line 47 of file RestartableModelInterface.h.

Referenced by SurrogateTrainerOutput::output(), and MappingOutput::output().

47 { return _model_meta_data_name; }

RestartableModelInterface::_model_meta_data_name

const std::string _model_meta_data_name

The model meta data name.

Definition: RestartableModelInterface.h:61

◆ validParams()

InputParameters LibtorchANNSurrogate::validParams ( )

static

Definition at line 17 of file LibtorchANNSurrogate.C.

 {
   InputParameters params = SurrogateModel::validParams();
   params.addClassDescription("Surrogate that evaluates a feedforward artificial neural net. ");
   return params;
 }

Member Data Documentation

◆ _input_standardizer

const StochasticTools::Standardizer& LibtorchANNSurrogate::_input_standardizer

protected

Standardizer for use with input (x)

Definition at line 33 of file LibtorchANNSurrogate.h.

Referenced by evaluate().

◆ _nn

const std::shared_ptr<Moose::LibtorchArtificialNeuralNet>& LibtorchANNSurrogate::_nn

protected

Pointer to the neural net object (initialized as null)

Definition at line 30 of file LibtorchANNSurrogate.h.

Referenced by evaluate().

◆ _output_standardizer

const StochasticTools::Standardizer& LibtorchANNSurrogate::_output_standardizer

protected

Standardizer for use with output response (y)

Definition at line 36 of file LibtorchANNSurrogate.h.

Referenced by evaluate().

The documentation for this class was generated from the following files:

stochastic_tools/include/libtorch/surrogates/LibtorchANNSurrogate.h
stochastic_tools/src/libtorch/surrogates/LibtorchANNSurrogate.C

Public Types

Public Member Functions

Static Public Member Functions

Public Attributes

Protected Attributes

Detailed Description

Constructor & Destructor Documentation

◆ LibtorchANNSurrogate()

Member Function Documentation

◆ declareModelData()

◆ defaultPredictorTypes()

◆ defaultResponseTypes()

◆ evaluate() [1/8]

◆ evaluate() [2/8]

◆ evaluate() [3/8]

◆ evaluate() [4/8]

◆ evaluate() [5/8]

◆ evaluate() [6/8]

◆ evaluate() [7/8]

◆ evaluate() [8/8]

◆ getModelData()

◆ getModelDataFileName()

◆ getSurrogateModel() [1/2]

◆ getSurrogateModel() [2/2]

◆ getSurrogateModelByName() [1/2]

◆ getSurrogateModelByName() [2/2]

◆ getSurrogateTrainer() [1/2]

◆ getSurrogateTrainer() [2/2]

◆ getSurrogateTrainerByName() [1/2]

◆ getSurrogateTrainerByName() [2/2]

◆ hasModelData()

◆ modelMetaDataName()

◆ validParams()

Member Data Documentation

◆ _input_standardizer

◆ _nn

◆ _output_standardizer