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

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