Abstract:The requirement for large-scale global simulations of plasma is an ongoing challenge in both space and laboratory plasma physics. Any simulation based on a fluid model inherently requires a closure relation for the high order plasma moments. This review compiles and analyses the recent surge of machine learning approaches developing improved plasma closure models capable of capturing kinetic phenomena within plasma fluid models. The purpose of this review is both to collect and analyse the various methods employed on the plasma closure problem, including both equation discovery methods and neural network surrogate approaches, as well as to provide a general overview of the state of the problem. In particular, we outline the challenges associated with machine learning based closure relations and the direction that future research might take in order to address these challenges.
| Comments: | 56 pages, 6 figures |
| Subjects: | Plasma Physics (physics.plasm-ph); Machine Learning (cs.LG) |
| Cite as: | arXiv:2511.22486 [physics.plasm-ph] |
| (or arXiv:2511.22486v2 [physics.plasm-ph] for this version) | |
| https://doi.org/10.48550/arXiv.2511.22486 arXiv-issued DOI via DataCite |
From: Sam Burles Mr [view email]
[v1]
Thu, 27 Nov 2025 14:20:36 UTC (839 KB)
[v2]
Fri, 17 Apr 2026 13:21:54 UTC (1,425 KB)
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