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arXiv · 2510.23088

Machine Learning approach to modeling of neutral particles transport in plasma

M. V. Umansky·G. J. Parker·R. D. Smirnov
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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.

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M. V. Umansky, G. J. Parker, R. D. Smirnov. 2025-10-27. Machine Learning approach to modeling of neutral particles transport in plasma. https://arxiv.org/abs/2510.23088

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