Poster

Structure Preserving Neural Networks: A Case Study in the Entropy Closure of the Boltzmann Equation

Steffen Schotthöfer · Tianbai Xiao · Martin Frank · Cory Hauck

Hall E #100

Keywords: [ PM: Structure Learning ] [ OPT: Sampling and Optimization ] [ OPT: Convex ] [ MISC: Supervised Learning ] [ APP: Physics ]


Abstract:

In this paper, we explore applications of deep learning in statistical physics. We choose the Boltzmann equation as a typical example, where neural networks serve as a closure to its moment system. We present two types of neural networks to embed the convexity of entropy and to preserve the minimum entropy principle and intrinsic mathematical structures of the moment system of the Boltzmann equation. We derive an error bound for the generalization gap of convex neural networks which are trained in Sobolev norm and use the results to construct data sampling methods for neural network training. Numerical experiments demonstrate that the neural entropy closure is significantly faster than classical optimizers while maintaining sufficient accuracy.

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