The Machine Learning Approach to Moment Closure Relations for Plasma: A Review
It provides a comprehensive overview for researchers in plasma physics, but is incremental as it reviews existing methods rather than introducing new ones.
This review addresses the challenge of developing closure relations for plasma fluid models by compiling and analyzing recent machine learning approaches, including equation discovery and neural network surrogates, to capture kinetic phenomena and enable large-scale global simulations.
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 highlight the challenges of developing a data-driven closure as well as the direction future work should take toward addressing these challenges, in the pursuit of a computationally viable large-scale global simulation.