Covariance System

Overview

Some surrogate models allow for a covariance function (often referred to as kernel functions or simply kernels) to project the input parameters into a different input space. The Gaussian process surrogate is an example of such a surrogate. These covariance functions can be defined in the [Covariance] block.

Creating a Covariance Function

A covariance function is created by inheriting from CovarainceFunctionBase and overriding the methods in the base class.

Using a Covariance Function

In a Trainer

Within the surrogate/trainer framework in the Stochastic Tools module, the covariance function will often be used within the trainer object. The trainer should inherit from the CovarianceInterface class to enable determining the covariance object by name.

class GaussianProcessTrainer : public SurrogateTrainer, public CovarianceInterface

Because many trainers which use a covariance function should be compatible with many different types of covariance functions, polymorphism of C++ pointers are often used.

  CovarianceFunctionBase * _covariance_function;

The covariance can be found by name using the interface

    _covariance_function(

In a Surrogate

The covariance function may also be used in the surrogate. For convenience the surrogate is able to determine the correct covariance used in the training step.

If the surrogate is given the trainer (not loaded from a file) the covariance function can be linked via the trainer object.

    _covariance_function(
        isParamValid("trainer")
            ? dynamic_cast<GaussianProcessTrainer &>(getSurrogateTrainer("trainer")).getCovarPtr()
            : nullptr)

If the surrogate loads the training data from a file, the LoadCovarianceDataAction automatically reconstructs the covariance object used in the training phase, and calls the surrogate setupCovariance() method to make the linkage. This recreation is done by storing the buildHyperParamMap() in the trainer, and storing the hyperparameters for use in the surrogate.

Example Input File Syntax

[Covariance]
  [covar]
    type = SquaredExponentialCovariance
    signal_variance = 1 #Use a signal variance of 1 in the kernel
    noise_variance = 1e-6 #A small amount of noise can help with numerical stability
    length_factor = '0.38971 0.38971' #Select a length factor for each parameter (k and q)
  []
[]

Available Objects

Stochastic Tools App
ExponentialCovarianceExponential covariance function.
MaternHalfIntCovarianceMatern half-integer covariance function.
SquaredExponentialCovarianceSquared Exponential covariance function.

Available Actions

Stochastic Tools App
AddCovarianceActionAdds Covariance objects contained within the [Trainers] and [Surrogates] input blocks.

(modules/stochastic_tools/include/surrogates/GaussianProcessTrainer.h)

// This file is part of the MOOSE framework
// https://www.mooseframework.org
//
// All rights reserved, see COPYRIGHT for full restrictions
// https://github.com/idaholab/moose/blob/master/COPYRIGHT
//
// Licensed under LGPL 2.1, please see LICENSE for details
// https://www.gnu.org/licenses/lgpl-2.1.html

#pragma once

#include "SurrogateTrainer.h"
#include "Standardizer.h"
#include <Eigen/Dense>

#include "Distribution.h"

#include "CovarianceFunctionBase.h"
#include "CovarianceInterface.h"

class GaussianProcessTrainer : public SurrogateTrainer, public CovarianceInterface
{
public:
  static InputParameters validParams();
  GaussianProcessTrainer(const InputParameters & parameters);
  virtual void preTrain() override;
  virtual void train() override;
  virtual void postTrain() override;

  CovarianceFunctionBase * getCovarPtr() const { return _covariance_function; }

#ifdef LIBMESH_HAVE_PETSC
  // Routine to perform hyperparameter tuning
  PetscErrorCode hyperparamTuning();

  PetscErrorCode FormInitialGuess(GaussianProcessTrainer * GP_ptr, Vec theta);

  // Wrapper for PETSc function callback
  static PetscErrorCode
  FormFunctionGradientWrapper(Tao tao, Vec theta, PetscReal * f, Vec Grad, void * ptr);

  // Computes Gradient of the loss function
  void FormFunctionGradient(Tao tao, Vec theta, PetscReal * f, Vec Grad);

  // Sets bounds for hyperparameters
  void buildHyperParamBounds(libMesh::PetscVector<Number> & theta_l,
                             libMesh::PetscVector<Number> & theta_u) const;
  // write stored hyperparam_vecs to PetscVec
  void mapToVec(libMesh::PetscVector<Number> & theta) const;

  // loads PetscVec to stored hyperparam_vecs
  void vecToMap(libMesh::PetscVector<Number> & theta);
#endif

private:
  /// Paramaters (x) used for training, along with statistics
  RealEigenMatrix & _training_params;

  /// Standardizer for use with params (x)
  StochasticTools::Standardizer & _param_standardizer;

  /// Standardizer for use with data (y)
  StochasticTools::Standardizer & _data_standardizer;

  /// An _n_sample by _n_sample covariance matrix constructed from the selected kernel function
  RealEigenMatrix & _K;

  /// A solve of Ax=b via Cholesky.
  RealEigenMatrix & _K_results_solve;

  /// Cholesky decomposition Eigen object
  Eigen::LLT<RealEigenMatrix> & _K_cho_decomp;

  /// Switch for training param (x) standardization
  bool _standardize_params;

  /// Switch for training data(y) standardization
  bool _standardize_data;

  /// Covariance function object
  CovarianceFunctionBase * _covariance_function;

  /// Type of covariance function used for this surrogate
  std::string & _covar_type;

#ifdef LIBMESH_HAVE_PETSC
  /// Flag to toggle hyperparameter tuning/optimization
  bool _do_tuning;

  /// Command line options to feed to TAO optimization
  std::string _tao_options;

  /// Flag to toggle printing of TAO output
  bool _show_tao;

  /// Tao Communicator
  Parallel::Communicator _tao_comm;

  /// Contains tuning inforation. Index of hyperparam, size, and min/max bounds
  std::unordered_map<std::string, std::tuple<unsigned int, unsigned int, Real, Real>> _tuning_data;

  /// Number of tunable hyperparameters
  unsigned int _num_tunable;
#endif

  /// Scalar hyperparameters. Stored for use in surrogate
  std::unordered_map<std::string, Real> & _hyperparam_map;

  /// Vector hyperparameters. Stored for use in surrogate
  std::unordered_map<std::string, std::vector<Real>> & _hyperparam_vec_map;

  /// Data from the current sampler row
  const std::vector<Real> & _sampler_row;

  /// Response value
  const Real & _rval;

  /// Predictor values from reporters
  std::vector<const Real *> _pvals;

  /// Columns from sampler for predictors
  std::vector<unsigned int> _pcols;

  /// Total number of parameters/dimensions
  unsigned int _n_params;

  /// Data (y) used for training
  RealEigenMatrix _training_data;
};

template <>
void dataStore(std::ostream & stream, Eigen::LLT<RealEigenMatrix> & decomp, void * context);
template <>
void dataLoad(std::istream & stream, Eigen::LLT<RealEigenMatrix> & decomp, void * context);

(modules/stochastic_tools/include/surrogates/GaussianProcessTrainer.h)

// This file is part of the MOOSE framework
// https://www.mooseframework.org
//
// All rights reserved, see COPYRIGHT for full restrictions
// https://github.com/idaholab/moose/blob/master/COPYRIGHT
//
// Licensed under LGPL 2.1, please see LICENSE for details
// https://www.gnu.org/licenses/lgpl-2.1.html

#pragma once

#include "SurrogateTrainer.h"
#include "Standardizer.h"
#include <Eigen/Dense>

#include "Distribution.h"

#include "CovarianceFunctionBase.h"
#include "CovarianceInterface.h"

class GaussianProcessTrainer : public SurrogateTrainer, public CovarianceInterface
{
public:
  static InputParameters validParams();
  GaussianProcessTrainer(const InputParameters & parameters);
  virtual void preTrain() override;
  virtual void train() override;
  virtual void postTrain() override;

  CovarianceFunctionBase * getCovarPtr() const { return _covariance_function; }

#ifdef LIBMESH_HAVE_PETSC
  // Routine to perform hyperparameter tuning
  PetscErrorCode hyperparamTuning();

  PetscErrorCode FormInitialGuess(GaussianProcessTrainer * GP_ptr, Vec theta);

  // Wrapper for PETSc function callback
  static PetscErrorCode
  FormFunctionGradientWrapper(Tao tao, Vec theta, PetscReal * f, Vec Grad, void * ptr);

  // Computes Gradient of the loss function
  void FormFunctionGradient(Tao tao, Vec theta, PetscReal * f, Vec Grad);

  // Sets bounds for hyperparameters
  void buildHyperParamBounds(libMesh::PetscVector<Number> & theta_l,
                             libMesh::PetscVector<Number> & theta_u) const;
  // write stored hyperparam_vecs to PetscVec
  void mapToVec(libMesh::PetscVector<Number> & theta) const;

  // loads PetscVec to stored hyperparam_vecs
  void vecToMap(libMesh::PetscVector<Number> & theta);
#endif

private:
  /// Paramaters (x) used for training, along with statistics
  RealEigenMatrix & _training_params;

  /// Standardizer for use with params (x)
  StochasticTools::Standardizer & _param_standardizer;

  /// Standardizer for use with data (y)
  StochasticTools::Standardizer & _data_standardizer;

  /// An _n_sample by _n_sample covariance matrix constructed from the selected kernel function
  RealEigenMatrix & _K;

  /// A solve of Ax=b via Cholesky.
  RealEigenMatrix & _K_results_solve;

  /// Cholesky decomposition Eigen object
  Eigen::LLT<RealEigenMatrix> & _K_cho_decomp;

  /// Switch for training param (x) standardization
  bool _standardize_params;

  /// Switch for training data(y) standardization
  bool _standardize_data;

  /// Covariance function object
  CovarianceFunctionBase * _covariance_function;

  /// Type of covariance function used for this surrogate
  std::string & _covar_type;

#ifdef LIBMESH_HAVE_PETSC
  /// Flag to toggle hyperparameter tuning/optimization
  bool _do_tuning;

  /// Command line options to feed to TAO optimization
  std::string _tao_options;

  /// Flag to toggle printing of TAO output
  bool _show_tao;

  /// Tao Communicator
  Parallel::Communicator _tao_comm;

  /// Contains tuning inforation. Index of hyperparam, size, and min/max bounds
  std::unordered_map<std::string, std::tuple<unsigned int, unsigned int, Real, Real>> _tuning_data;

  /// Number of tunable hyperparameters
  unsigned int _num_tunable;
#endif

  /// Scalar hyperparameters. Stored for use in surrogate
  std::unordered_map<std::string, Real> & _hyperparam_map;

  /// Vector hyperparameters. Stored for use in surrogate
  std::unordered_map<std::string, std::vector<Real>> & _hyperparam_vec_map;

  /// Data from the current sampler row
  const std::vector<Real> & _sampler_row;

  /// Response value
  const Real & _rval;

  /// Predictor values from reporters
  std::vector<const Real *> _pvals;

  /// Columns from sampler for predictors
  std::vector<unsigned int> _pcols;

  /// Total number of parameters/dimensions
  unsigned int _n_params;

  /// Data (y) used for training
  RealEigenMatrix _training_data;
};

template <>
void dataStore(std::ostream & stream, Eigen::LLT<RealEigenMatrix> & decomp, void * context);
template <>
void dataLoad(std::istream & stream, Eigen::LLT<RealEigenMatrix> & decomp, void * context);

(modules/stochastic_tools/src/surrogates/GaussianProcessTrainer.C)

// This file is part of the MOOSE framework
// https://www.mooseframework.org
//
// All rights reserved, see COPYRIGHT for full restrictions
// https://github.com/idaholab/moose/blob/master/COPYRIGHT
//
// Licensed under LGPL 2.1, please see LICENSE for details
// https://www.gnu.org/licenses/lgpl-2.1.html

#include "GaussianProcessTrainer.h"
#include "Sampler.h"
#include "CartesianProduct.h"

#include <petsctao.h>
#include <petscdmda.h>

#include "libmesh/petsc_vector.h"
#include "libmesh/petsc_matrix.h"

#include <math.h>

registerMooseObject("StochasticToolsApp", GaussianProcessTrainer);

InputParameters
GaussianProcessTrainer::validParams()
{
  InputParameters params = SurrogateTrainer::validParams();
  params += CovarianceInterface::validParams();
  params.addClassDescription(
      "Provides data preperation and training for a Gaussian Process surrogate model.");
  params.addRequiredParam<ReporterName>(
      "response", "Reporter value of response results, can be vpp with <vpp_name>/<vector_name>.");
  params.addParam<std::vector<ReporterName>>(
      "predictors",
      std::vector<ReporterName>(),
      "Reporter values used as the independent random variables, If 'predictors' and "
      "'predictor_cols' are both empty, all sampler columns are used.");
  params.addParam<std::vector<unsigned int>>(
      "predictor_cols",
      std::vector<unsigned int>(),
      "Sampler columns used as the independent random variables, If 'predictors' and "
      "'predictor_cols' are both empty, all sampler columns are used.");
  params.addRequiredParam<UserObjectName>("covariance_function", "Name of covariance function.");
  params.addParam<bool>(
      "standardize_params", true, "Standardize (center and scale) training parameters (x values)");
  params.addParam<bool>(
      "standardize_data", true, "Standardize (center and scale) training data (y values)");
#ifdef LIBMESH_HAVE_PETSC
  params.addParam<std::string>(
      "tao_options", "", "Command line options for PETSc/TAO hyperparameter optimization");
  params.addParam<bool>("show_tao", false, "Switch to show TAO solver results");
  params.addParam<std::vector<std::string>>("tune_parameters",
                                            "Select hyperparameters to be tuned");
  params.addParam<std::vector<Real>>("tuning_min", "Minimum allowable tuning value");
  params.addParam<std::vector<Real>>("tuning_max", "Maximum allowable tuning value");
#endif
  return params;
}

GaussianProcessTrainer::GaussianProcessTrainer(const InputParameters & parameters)
  : SurrogateTrainer(parameters),
    CovarianceInterface(parameters),
    _training_params(declareModelData<RealEigenMatrix>("_training_params")),
    _param_standardizer(declareModelData<StochasticTools::Standardizer>("_param_standardizer")),
    _data_standardizer(declareModelData<StochasticTools::Standardizer>("_data_standardizer")),
    _K(declareModelData<RealEigenMatrix>("_K")),
    _K_results_solve(declareModelData<RealEigenMatrix>("_K_results_solve")),
    _K_cho_decomp(declareModelData<Eigen::LLT<RealEigenMatrix>>("_K_cho_decomp")),
    _standardize_params(getParam<bool>("standardize_params")),
    _standardize_data(getParam<bool>("standardize_data")),
    _covariance_function(
        getCovarianceFunctionByName(getParam<UserObjectName>("covariance_function"))),
    _covar_type(declareModelData<std::string>("_covar_type", _covariance_function->type())),
#ifdef LIBMESH_HAVE_PETSC
    _do_tuning(isParamValid("tune_parameters")),
    _tao_options(getParam<std::string>("tao_options")),
    _show_tao(getParam<bool>("show_tao")),
    _tao_comm(MPI_COMM_SELF),
#endif
    _hyperparam_map(declareModelData<std::unordered_map<std::string, Real>>("_hyperparam_map")),
    _hyperparam_vec_map(declareModelData<std::unordered_map<std::string, std::vector<Real>>>(
        "_hyperparam_vec_map")),
    _sampler_row(getSamplerData()),
    _rval(getTrainingData<Real>(getParam<ReporterName>("response"))),
    _pvals(getParam<std::vector<ReporterName>>("predictors").size()),
    _pcols(getParam<std::vector<unsigned int>>("predictor_cols")),
    _n_params((_pvals.empty() && _pcols.empty()) ? _sampler.getNumberOfCols()
                                                 : (_pvals.size() + _pcols.size()))
{
  const auto & pnames = getParam<std::vector<ReporterName>>("predictors");
  for (unsigned int i = 0; i < pnames.size(); ++i)
    _pvals[i] = &getTrainingData<Real>(pnames[i]);

  // If predictors and predictor_cols are empty, use all sampler columns
  if (_pvals.empty() && _pcols.empty())
  {
    _pcols.resize(_sampler.getNumberOfCols());
    std::iota(_pcols.begin(), _pcols.end(), 0);
  }

#ifdef LIBMESH_HAVE_PETSC
  _num_tunable = 0;
  std::vector<std::string> tune_parameters(getParam<std::vector<std::string>>("tune_parameters"));
  // Error Checking
  if (isParamValid("tuning_min") &&
      (getParam<std::vector<Real>>("tuning_min").size() != tune_parameters.size()))
    mooseError("tuning_min size does not match tune_parameters");
  if (isParamValid("tuning_max") &&
      (getParam<std::vector<Real>>("tuning_max").size() != tune_parameters.size()))
    mooseError("tuning_max size does not match tune_parameters");
  // Fill Out Tunable Paramater information
  for (unsigned int ii = 0; ii < tune_parameters.size(); ++ii)
  {
    const auto & hp = tune_parameters[ii];
    if (_covariance_function->isTunable(hp))
    {
      unsigned int size;
      Real min;
      Real max;
      // Get size and default min/max
      _covariance_function->getTuningData(hp, size, min, max);
      // Check for overridden min/max
      min = isParamValid("tuning_min") ? getParam<std::vector<Real>>("tuning_min")[ii] : min;
      max = isParamValid("tuning_max") ? getParam<std::vector<Real>>("tuning_max")[ii] : max;
      // Save data in tuple
      _tuning_data[hp] = std::make_tuple(_num_tunable, size, min, max);
      _num_tunable += size;
    }
  }
#endif
}

void
GaussianProcessTrainer::preTrain()
{
  _training_params.setZero(_sampler.getNumberOfRows(), _n_params);
  _training_data.setZero(_sampler.getNumberOfRows(), 1);
}

void
GaussianProcessTrainer::train()
{
  unsigned int d = 0;
  for (const auto & val : _pvals)
    _training_params(_row, d++) = *val;
  for (const auto & col : _pcols)
    _training_params(_row, d++) = _sampler_row[col];

  // Loading result data from response reporter
  _training_data(_row, 0) = _rval;
}

void
GaussianProcessTrainer::postTrain()
{
  for (unsigned int ii = 0; ii < _training_params.rows(); ++ii)
  {
    for (unsigned int jj = 0; jj < _training_params.cols(); ++jj)
      gatherSum(_training_params(ii, jj));
    gatherSum(_training_data(ii, 0));
  }

  // Standardize (center and scale) training params
  if (_standardize_params)
  {
    _param_standardizer.computeSet(_training_params);
    _param_standardizer.getStandardized(_training_params);
  }
  // if not standardizing data set mean=0, std=1 for use in surrogate
  else
    _param_standardizer.set(0, 1, _n_params);

  // Standardize (center and scale) training data
  if (_standardize_data)
  {
    _data_standardizer.computeSet(_training_data);
    _data_standardizer.getStandardized(_training_data);
  }
  // if not standardizing data set mean=0, std=1 for use in surrogate
  else
    _param_standardizer.set(0, 1, _n_params);

  _K.resize(_training_params.rows(), _training_params.rows());

#ifdef LIBMESH_HAVE_PETSC
  if (_do_tuning)
    if (hyperparamTuning())
      mooseError("PETSc/TAO error in hyperparameter tuning.");
#endif

  _covariance_function->computeCovarianceMatrix(_K, _training_params, _training_params, true);
  _K_cho_decomp = _K.llt();
  _K_results_solve = _K_cho_decomp.solve(_training_data);

  _covariance_function->buildHyperParamMap(_hyperparam_map, _hyperparam_vec_map);
}

#ifdef LIBMESH_HAVE_PETSC
PetscErrorCode
GaussianProcessTrainer::hyperparamTuning()
{
  PetscErrorCode ierr;
  Tao tao;
  GaussianProcessTrainer * GP_ptr = this;

  // Setup Tao optimization problem
  ierr = TaoCreate(_tao_comm.get(), &tao);
  CHKERRQ(ierr);
  ierr = PetscOptionsSetValue(NULL, "-tao_type", "bncg");
  CHKERRQ(ierr);
  ierr = PetscOptionsInsertString(NULL, _tao_options.c_str());
  CHKERRQ(ierr);
  ierr = TaoSetFromOptions(tao);
  CHKERRQ(ierr);

  // Define petsc vetor to hold tunalbe hyper-params
  libMesh::PetscVector<Number> theta(_tao_comm, _num_tunable);
  ierr = GaussianProcessTrainer::FormInitialGuess(GP_ptr, theta.vec());
  CHKERRQ(ierr);
  ierr = TaoSetInitialVector(tao, theta.vec());
  CHKERRQ(ierr);

  // Get Hyperparameter bounds.
  libMesh::PetscVector<Number> lower(_tao_comm, _num_tunable);
  libMesh::PetscVector<Number> upper(_tao_comm, _num_tunable);
  buildHyperParamBounds(lower, upper);
  CHKERRQ(ierr);
  ierr = TaoSetVariableBounds(tao, lower.vec(), upper.vec());
  CHKERRQ(ierr);

  // Set Objective and Graident Callback
  ierr = TaoSetObjectiveAndGradientRoutine(
      tao, GaussianProcessTrainer::FormFunctionGradientWrapper, (void *)this);
  CHKERRQ(ierr);

  // Solve
  ierr = TaoSolve(tao);
  CHKERRQ(ierr);
  //
  if (_show_tao)
  {
    ierr = TaoView(tao, PETSC_VIEWER_STDOUT_WORLD);
    theta.print();
  }

  _covariance_function->loadHyperParamMap(_hyperparam_map, _hyperparam_vec_map);

  ierr = TaoDestroy(&tao);
  CHKERRQ(ierr);

  return 0;
}

PetscErrorCode
GaussianProcessTrainer::FormInitialGuess(GaussianProcessTrainer * GP_ptr, Vec theta_vec)
{
  libMesh::PetscVector<Number> theta(theta_vec, GP_ptr->_tao_comm);
  _covariance_function->buildHyperParamMap(_hyperparam_map, _hyperparam_vec_map);
  mapToVec(theta);
  return 0;
}

PetscErrorCode
GaussianProcessTrainer::FormFunctionGradientWrapper(
    Tao tao, Vec theta_vec, PetscReal * f, Vec grad_vec, void * ptr)
{
  GaussianProcessTrainer * GP_ptr = (GaussianProcessTrainer *)ptr;
  GP_ptr->FormFunctionGradient(tao, theta_vec, f, grad_vec);
  return 0;
}

void
GaussianProcessTrainer::FormFunctionGradient(Tao /*tao*/,
                                             Vec theta_vec,
                                             PetscReal * f,
                                             Vec grad_vec)
{
  libMesh::PetscVector<Number> theta(theta_vec, _tao_comm);
  libMesh::PetscVector<Number> grad(grad_vec, _tao_comm);

  vecToMap(theta);
  _covariance_function->loadHyperParamMap(_hyperparam_map, _hyperparam_vec_map);
  _covariance_function->computeCovarianceMatrix(_K, _training_params, _training_params, true);
  _K_cho_decomp = _K.llt();
  _K_results_solve = _K_cho_decomp.solve(_training_data);

  // testing auto tuning
  RealEigenMatrix dKdhp(_training_params.rows(), _training_params.rows());
  RealEigenMatrix alpha = _K_results_solve * _K_results_solve.transpose();
  for (auto iter = _tuning_data.begin(); iter != _tuning_data.end(); ++iter)
  {
    std::string hyper_param_name = iter->first;
    for (unsigned int ii = 0; ii < std::get<1>(iter->second); ++ii)
    {
      _covariance_function->computedKdhyper(dKdhp, _training_params, hyper_param_name, ii);
      RealEigenMatrix tmp = alpha * dKdhp - _K_cho_decomp.solve(dKdhp);
      grad.set(std::get<0>(iter->second) + ii, -tmp.trace() / 2.0);
    }
  }
  //
  Real log_likelihood = 0;
  log_likelihood += -(_training_data.transpose() * _K_results_solve)(0, 0);
  log_likelihood += -std::log(_K.determinant());
  log_likelihood += -_training_data.rows() * std::log(2 * M_PI);
  log_likelihood = -log_likelihood / 2;
  *f = log_likelihood;
}

void
GaussianProcessTrainer::buildHyperParamBounds(libMesh::PetscVector<Number> & theta_l,
                                              libMesh::PetscVector<Number> & theta_u) const
{
  for (auto iter = _tuning_data.begin(); iter != _tuning_data.end(); ++iter)
  {
    for (unsigned int ii = 0; ii < std::get<1>(iter->second); ++ii)
    {
      theta_l.set(std::get<0>(iter->second) + ii, std::get<2>(iter->second));
      theta_u.set(std::get<0>(iter->second) + ii, std::get<3>(iter->second));
    }
  }
}

void
GaussianProcessTrainer::mapToVec(libMesh::PetscVector<Number> & theta) const
{
  for (auto iter = _tuning_data.begin(); iter != _tuning_data.end(); ++iter)
  {
    std::string hyper_param_name = iter->first;
    if (_hyperparam_map.find(hyper_param_name) != _hyperparam_map.end())
    {
      theta.set(std::get<0>(iter->second), _hyperparam_map[hyper_param_name]);
    }
    else if (_hyperparam_vec_map.find(hyper_param_name) != _hyperparam_vec_map.end())
    {
      for (unsigned int ii = 0; ii < std::get<1>(iter->second); ++ii)
      {
        theta.set(std::get<0>(iter->second) + ii, _hyperparam_vec_map[hyper_param_name][ii]);
      }
    }
  }
}

void
GaussianProcessTrainer::vecToMap(libMesh::PetscVector<Number> & theta)
{
  for (auto iter = _tuning_data.begin(); iter != _tuning_data.end(); ++iter)
  {
    std::string hyper_param_name = iter->first;
    if (_hyperparam_map.find(hyper_param_name) != _hyperparam_map.end())
    {
      _hyperparam_map[hyper_param_name] = theta(std::get<0>(iter->second));
    }
    else if (_hyperparam_vec_map.find(hyper_param_name) != _hyperparam_vec_map.end())
    {
      for (unsigned int ii = 0; ii < std::get<1>(iter->second); ++ii)
      {
        _hyperparam_vec_map[hyper_param_name][ii] = theta(std::get<0>(iter->second) + ii);
      }
    }
  }
}
#endif // LIBMESH_HAVE_PETSC

template <>
void
dataStore(std::ostream & stream, Eigen::LLT<RealEigenMatrix> & decomp, void * context)
{
  // Store the L matrix as opposed to the full matrix to avoid compounding
  // roundoff error and decomposition error
  RealEigenMatrix L(decomp.matrixL());
  dataStore(stream, L, context);
}

template <>
void
dataLoad(std::istream & stream, Eigen::LLT<RealEigenMatrix> & decomp, void * context)
{
  RealEigenMatrix L;
  dataLoad(stream, L, context);
  decomp.compute(L * L.transpose());
}

(modules/stochastic_tools/src/surrogates/GaussianProcess.C)

// This file is part of the MOOSE framework
// https://www.mooseframework.org
//
// All rights reserved, see COPYRIGHT for full restrictions
// https://github.com/idaholab/moose/blob/master/COPYRIGHT
//
// Licensed under LGPL 2.1, please see LICENSE for details
// https://www.gnu.org/licenses/lgpl-2.1.html

#include "GaussianProcess.h"
#include "Sampler.h"

#include "CovarianceFunctionBase.h"

registerMooseObject("StochasticToolsApp", GaussianProcess);

InputParameters
GaussianProcess::validParams()
{
  InputParameters params = SurrogateModel::validParams();
  params += CovarianceInterface::validParams();
  params.addClassDescription("Computes and evaluates Gaussian Process surrogate model.");
  return params;
}

GaussianProcess::GaussianProcess(const InputParameters & parameters)
  : SurrogateModel(parameters),
    CovarianceInterface(parameters),
    _training_params(getModelData<RealEigenMatrix>("_training_params")),
    _param_standardizer(getModelData<StochasticTools::Standardizer>("_param_standardizer")),
    _data_standardizer(getModelData<StochasticTools::Standardizer>("_data_standardizer")),
    _K(getModelData<RealEigenMatrix>("_K")),
    _K_results_solve(getModelData<RealEigenMatrix>("_K_results_solve")),
    _K_cho_decomp(getModelData<Eigen::LLT<RealEigenMatrix>>("_K_cho_decomp")),
    _covar_type(getModelData<std::string>("_covar_type")),
    _hyperparam_map(getModelData<std::unordered_map<std::string, Real>>("_hyperparam_map")),
    _hyperparam_vec_map(
        getModelData<std::unordered_map<std::string, std::vector<Real>>>("_hyperparam_vec_map")),
    _covariance_function(
        isParamValid("trainer")
            ? dynamic_cast<GaussianProcessTrainer &>(getSurrogateTrainer("trainer")).getCovarPtr()
            : nullptr)
{
}

void
GaussianProcess::setupCovariance(UserObjectName covar_name)
{
  if (_covariance_function != nullptr)
    ::mooseError("Attempting to redefine covariance function using setupCovariance.");
  _covariance_function = getCovarianceFunctionByName(covar_name);
}

Real
GaussianProcess::evaluate(const std::vector<Real> & x) const
{
  // Overlaod for evaluate to maintain general compatibility. Only returns mean
  Real dummy = 0;
  return this->evaluate(x, dummy);
}

Real
GaussianProcess::evaluate(const std::vector<Real> & x, Real & std_dev) const
{

  unsigned int _n_params = _training_params.cols();
  unsigned int _num_tests = 1;

  mooseAssert(x.size() == _n_params,
              "Number of parameters provided for evaluation does not match number of parameters "
              "used for training.");

  RealEigenMatrix test_points(_num_tests, _n_params);
  for (unsigned int ii = 0; ii < _n_params; ++ii)
    test_points(0, ii) = x[ii];

  _param_standardizer.getStandardized(test_points);

  RealEigenMatrix K_train_test(_training_params.rows(), test_points.rows());
  _covariance_function->computeCovarianceMatrix(K_train_test, _training_params, test_points, false);
  RealEigenMatrix K_test(test_points.rows(), test_points.rows());
  _covariance_function->computeCovarianceMatrix(K_test, test_points, test_points, true);

  // Compute the predicted mean value (centered)
  RealEigenMatrix pred_value = (K_train_test.transpose() * _K_results_solve);
  // De-center/scale the value and store for return
  _data_standardizer.getDestandardized(pred_value);

  RealEigenMatrix pred_var =
      K_test - (K_train_test.transpose() * _K_cho_decomp.solve(K_train_test));

  // Vairance computed, take sqrt for standard deviation, scale up by training data std and store
  RealEigenMatrix std_dev_mat = pred_var.array().sqrt();
  _data_standardizer.getDescaled(std_dev_mat);
  std_dev = std_dev_mat(0, 0);

  return pred_value(0, 0);
}

(modules/stochastic_tools/test/tests/surrogates/gaussian_process/GP_squared_exponential_training.i)

[StochasticTools]
[]

[Distributions]
  [k_dist]
    type = Uniform
    lower_bound = 1
    upper_bound = 10
  []
  [q_dist]
    type = Uniform
    lower_bound = 9000
    upper_bound = 11000
  []
[]

[Samplers]
  [train_sample]
    type = MonteCarlo
    num_rows = 10
    distributions = 'k_dist q_dist'
    execute_on = PRE_MULTIAPP_SETUP
  []
[]

[MultiApps]
  [sub]
    type = SamplerFullSolveMultiApp
    input_files = sub.i
    sampler = train_sample
  []
[]

[Controls]
  [cmdline]
    type = MultiAppCommandLineControl
    multi_app = sub
    sampler = train_sample
    param_names = 'Materials/conductivity/prop_values Kernels/source/value'
  []
[]

[Transfers]
  [data]
    type = SamplerPostprocessorTransfer
    multi_app = sub
    sampler = train_sample
    to_vector_postprocessor = results
    from_postprocessor = 'avg'
  []
[]

[VectorPostprocessors]
  [results]
    type = StochasticResults
  []
[]

[Trainers]
  [GP_avg_trainer]
    type = GaussianProcessTrainer
    execute_on = timestep_end
    covariance_function = 'covar'             #Choose a squared exponential for the kernel
    standardize_params = 'true'               #Center and scale the training params
    standardize_data = 'true'                 #Center and scale the training data
    sampler = train_sample
    response = results/data:avg
  []
[]

[Covariance]
  [covar]
    type=SquaredExponentialCovariance
    signal_variance = 1                       #Use a signal variance of 1 in the kernel
    noise_variance = 1e-6                     #A small amount of noise can help with numerical stability
    length_factor = '0.38971 0.38971'         #Select a length factor for each parameter (k and q)
  []
[]

[Outputs]
  file_base = gauss_process_training
  [out]
    type = SurrogateTrainerOutput
    trainers = 'GP_avg_trainer'
    execute_on = FINAL
  []
[]

Overview
Creating a Covariance Function
Using a Covariance Function
Example Input File Syntax
Available Objects
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