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Journal Articles Kinetic and Related Models Year : 2021

A neural network closure for the Euler-Poisson system based on kinetic simulations

Abstract

This work deals with the modeling of plasmas, which are charged-particle fluids. Thanks to machine leaning, we construct a closure for the one-dimensional Euler-Poisson system valid for a wide range of collision regimes. This closure, based on a fully convolutional neural network called V-net, takes as input the whole spatial density, mean velocity and temperature and predicts as output the whole heat flux. It is learned from data coming from kinetic simulations of the Vlasov-Poisson equations. Data generation and preprocessings are designed to ensure an almost uniform accuracy over the chosen range of Knudsen numbers (which parametrize collision regimes). Finally, several numerical tests are carried out to assess validity and flexibility of the whole pipeline.
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Dates and versions

hal-02965954 , version 1 (13-10-2020)

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Leo Bois, Emmanuel Franck, Laurent Navoret, Vincent Vigon. A neural network closure for the Euler-Poisson system based on kinetic simulations. Kinetic and Related Models , 2021, 15 (1), pp.49-89. ⟨10.3934/krm.2021044⟩. ⟨hal-02965954⟩
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