AdaptiveMonteCarloDecision.h

Go to the documentation of this file.

//* This file is part of the MOOSE framework
//* https://mooseframework.inl.gov
//*
//* 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 "GeneralReporter.h"
#include "AdaptiveImportanceSampler.h"
#include "ParallelSubsetSimulation.h"
 
class AdaptiveMonteCarloDecision : public GeneralReporter
{
public:
  static InputParameters validParams();
  AdaptiveMonteCarloDecision(const InputParameters & parameters);
  virtual void initialize() override {}
  virtual void finalize() override {}
  virtual void execute() override;
 
protected:
  const std::vector<Real> & _output_value;
 
  std::vector<Real> & _output_required;
 
  std::vector<std::vector<Real>> & _inputs;
 
private:
  void reinitChain();
 
  Sampler & _sampler;
 
  const AdaptiveImportanceSampler * const _ais;
 
  const ParallelSubsetSimulation * const _pss;
 
  int & _check_step;
 
  std::vector<std::vector<Real>> & _prev_val;
 
  std::vector<Real> & _prev_val_out;
 
  std::vector<std::vector<Real>> & _inputs_sto;
 
  std::vector<std::vector<Real>> & _inputs_sorted;
 
  std::vector<Real> & _outputs_sto;
 
  std::vector<Real> & _output_sorted;
 
  Real & _output_limit;
 
  const bool _gp_used;
 
  const int * const _gp_training_samples;
};