Improved Monte Carlo Variance Reduction for Space and Energy Self-Shielding

Improved Monte Carlo Variance Reduction for Space and Energy Self-Shielding

Continued demand for accurate and computationally efficient transport methods to solve optically thick, fixed-source transport problems has inspired research on variance-reduction (VR) techniques for Monte Carlo (MC). Methods that use deterministic results to create VR maps for MC constitute a dominant branch of this research, with Forward Weighted–Consistent Adjoint Driven Importance Sampling ...

Investigation of Automated Variance Reduction Techniques for Monte Carlo Shielding Problems 22.106 Project Report

1. Introduction. The Monte Carlo method is widely believed to be the most accurate method for solving problems in radiation transport. Unfortunately, due to its very nature—following individual particle histories—certain classes of problems are particularly challenging for the method. One such class of problems consist of so-called deep penetration shielding problems. Because the purpose of a s...

Monte Carlo Integration and Variance Reduction

Variance Reduction Techniques in Monte Carlo Methods

Monte Carlo methods are simulation algorithms to estimate a numerical quantity in a statistical model of a real system. These algorithms are executed by computer programs. Variance reduction techniques (VRT) are needed, even though computer speed has been increasing dramatically, ever since the introduction of computers. This increased computer power has stimulated simulation analysts to develo...

Variance Reduction in Monte-Carlo Tree Search

Monte-Carlo Tree Search (MCTS) has proven to be a powerful, generic planning technique for decision-making in single-agent and adversarial environments. The stochastic nature of the Monte-Carlo simulations introduces errors in the value estimates, both in terms of bias and variance. Whilst reducing bias (typically through the addition of domain knowledge) has been studied in the MCTS literature...