Machine Learning approach to modeling of neutral particles transport in plasma

TL;DR

This paper introduces a neural network-based method to efficiently model neutral particle transport in plasma, leveraging a propagator approach for faster and more accurate simulations in fusion boundary conditions.

Contribution

It proposes a novel neural network model for the propagator in Monte Carlo simulations, enabling rapid and smooth dependence on plasma parameters for neutral particle transport modeling.

Findings

Initial results from a 1D test are promising

The approach allows for fast and accurate neutral distribution calculations

Potential for integration with Jacobian-based methods

Abstract

A propagator-based approach is investigated for Monte-Carlo (MC) modeling of neutral particles transport in fusion boundary plasmas. The propagator is essentially a Green function for the neutral kinetic equation, which depends on the plasma profiles. A Neural Network (NN) based model for the propagator provides a fast and accurate solution for the neutral distribution function in plasma. Furthermore, continuous and smooth dependence of NN-based reconstruction of the propagator on the plasma parameters opens the possibility for using this approach with Jacobian-based methods for time-integration and root finding. Initial results from a small 1D test problem look promising; however, important research questions are concerned with the scaling of the algorithm to larger systems.