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

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, they 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 plays a capital role in their accuracy. Published under an exclusive license by AIP Publishing.

Automated summary

Simulations of large-scale plasma systems often use a fluid approximation approach that lacks small-scale physical processes. This paper presents new closure terms constructed using machine learning techniques, which show promising results for capturing pressure tensor and heat flux in 2D magnetic reconnection simulations. The accuracy of the models is found to depend on the sampling of training points.