Machine Learning approach to modeling of neutral particles transport in plasma
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.
Keywords
Cite
@article{arxiv.2510.23088,
title = {Machine Learning approach to modeling of neutral particles transport in plasma},
author = {M. V. Umansky and G. J. Parker and R. D. Smirnov},
journal= {arXiv preprint arXiv:2510.23088},
year = {2026}
}