Structure Preserving Neural Network Based Entropy Closures for the Boltzmann Moment

In numerous applications, macroscopic simulations do not yield sufficient accuracy, thus physical modelling at a kinetic level is required. We consider the moment methods as a numerical solution method for the Boltzmann equation, a famous example for a kinetic equation. The minimal entropy closure is a closure for the Boltzmann moment system with desirable structural properties, that come at the expense of high computational cost. This talk presents neural network based acceleration strategies for the minimal entropy closure for the Boltzmann moment system.