Identification of high order closure terms from fully kinetic simulations using machine learning

TL;DR

This paper uses machine learning to develop new closure relations for fluid plasma models, aiming to better approximate kinetic effects and improve simulation accuracy.

Contribution

It introduces machine learning-based closure models for plasma simulations, outperforming traditional empirical closures in capturing kinetic physics.

Findings

ML models accurately predict pressure tensor components

Promising results for heat flux modeling

Sampling strategy significantly impacts model accuracy

Abstract

Simulations of large-scale plasma systems are typically based on a fluid approximation approach. These models construct a moment-based system of equations that approximate the particle-based physics as a fluid, but as a result lack the small-scale physical processes available to fully kinetic models. Traditionally, empirical closure relations are used to close the moment-based system of equations, which typically approximate the pressure tensor or heat flux. The more accurate the closure relation, the stronger the simulation approaches kinetic-based results. In this paper, new closure terms are constructed using machine learning techniques. Two different machine learning models, a multi-layer perceptron and a gradient boosting regressor, synthesize a local closure relation for the pressure tensor and heat flux vector from fully kinetic simulations of a 2D magnetic reconnection problem. The models are compared to an existing closure relation for the pressure tensor, and the applicability of the models is discussed. The initial results show that the models can capture the diagonal components of the pressure tensor accurately, and show promising results for the heat flux, opening the way for new experiments in multi-scale modeling. We find that the sampling of the points used to train both models play a capital role in their accuracy.