KUL/CmPA Seminar: Identification of high order closure terms from fully kinetic simulations using machine learning


KU Leuven Centre for mathematical Plasma-Astrophysics Seminar

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

Speaker: Brecht Laperre from CmPA

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, and show promising results for the heat flux, opening the way for new experiments in multi-scale modeling. 

​The seminar will be in a hybrid mode, in person in room 200B 00.07. The following (permanent) link may be used for online access:


Date and time: Thursday, January 13, 2022 - 14:00 to 15:00


Thursday, January 13, 2022 - 14:00 to 15:30

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