An Efficient Monte Carlo Simulation for Radiation Transport Based on Global Optimal Reference Field

TL;DR

This paper introduces an exact linear programming approach, solved efficiently with a graph neural network-based solver, to optimize the global reference field in Monte Carlo radiation transport simulations, significantly reducing variance and computational cost.

Contribution

It reformulates the global optimal reference field determination as an exact linear programming problem and employs a graph neural network-based solver for enhanced efficiency.

Findings

The MindOpt solver outperforms traditional methods in accuracy and speed.

The new approach reduces statistical errors in simulations with large temperature gradients.

Computational efficiency is significantly improved over previous approximate methods.

Abstract

The reference field method, known as the difference formulation, is a key variance reduction technique for Monte Carlo simulations of thermal radiation transport problems. When the material temperature is relatively high and the spatial temperature gradient is moderate, this method demonstrates significant advantages in reducing variance compared to classical Monte Carlo methods. However, in problems with larger temperature gradients, this method has not only been found ineffective at reducing statistical noise, but in some cases, it even increases noise compared to classical Monte Carlo methods. The global optimal reference field method, a recently proposed variance reduction technique, effectively reduces the average energy weight of Monte Carlo particles, thereby decreasing variance. Its effectiveness has been validated both theoretically and numerically, demonstrating a significant reduction in statistical errors for problems with large temperature gradients. In our previous work, instead of computing the exact global optimal reference field, we developed an approximate, physically motivated method to find a relatively better reference field using a selection scheme. In this work, we reformulate the problem of determining the global optimal reference field as a linear programming problem and solve it exactly. To further enhance computational efficiency, we use the MindOpt solver, which leverages graph neural network methods. Numerical experiments demonstrate that the MindOpt solver not only solves linear programming problems accurately but also significantly outperforms the Simplex and interior-point methods in terms of computational efficiency. The global optimal reference field method combined with the MindOpt solver not only improves computational efficiency but also substantially reduces statistical errors.