AdaptiveMonteCarloDecision.h

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//* 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"
#include "ActiveLearningGPDecision.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;

  libMesh::Parallel::Communicator & _local_comm;

  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;
};