Line data    Source code

       1             : //* This file is part of the MOOSE framework
       2             : //* https://www.mooseframework.org
       3             : //*
       4             : //* All rights reserved, see COPYRIGHT for full restrictions
       5             : //* https://github.com/idaholab/moose/blob/master/COPYRIGHT
       6             : //*
       7             : //* Licensed under LGPL 2.1, please see LICENSE for details
       8             : //* https://www.gnu.org/licenses/lgpl-2.1.html
       9             : 
      10             : #include "BayesianPosteriorTargeted.h"
      11             : #include <cmath>
      12             : 
      13             : registerMooseObject("StochasticToolsApp", BayesianPosteriorTargeted);
      14             : 
      15             : InputParameters
      16           8 : BayesianPosteriorTargeted::validParams()
      17             : {
      18           8 :   InputParameters params = ParallelAcquisitionFunctionBase::validParams();
      19           8 :   params.addClassDescription("Bayesian posterior targeted from El Gammal et al. 2023.");
      20           8 :   return params;
      21           0 : }
      22             : 
      23           4 : BayesianPosteriorTargeted::BayesianPosteriorTargeted(const InputParameters & parameters)
      24           4 :   : ParallelAcquisitionFunctionBase(parameters)
      25             : {
      26           4 : }
      27             : 
      28             : void
      29          16 : BayesianPosteriorTargeted::computeAcquisitionInternal(
      30             :     std::vector<Real> & acq,
      31             :     const std::vector<Real> & gp_mean,
      32             :     const std::vector<Real> & gp_std,
      33             :     const std::vector<std::vector<Real>> & test_inputs,
      34             :     const std::vector<std::vector<Real>> & /*train_inputs*/,
      35             :     const std::vector<Real> & /*generic*/) const
      36             : {
      37             :   Real psi = std::pow(test_inputs[0].size(), -0.85);
      38        1616 :   for (unsigned int i = 0; i < test_inputs.size(); ++i)
      39             :   {
      40        1600 :     acq[i] = std::exp(2.0 * psi * gp_mean[i]) * (std::exp(gp_std[i]) - 1.0);
      41        1600 :     if (std::isinf(acq[i]))
      42           0 :       acq[i] = std::numeric_limits<Real>::max();
      43             :   }
      44          16 : }