  The main challenge of large-scale numerical simulation of radiation transport
is the high memory and computation time requirements of discretization methods
for kinetic equations. In this work, we derive and investigate a neural
network-based approximation to the entropy closure method to accurately compute
the solution of the multi-dimensional moment system with a low memory footprint
and competitive computational time. We extend methods developed for the
standard entropy-based closure to the context of regularized entropy-based
closures. The main idea is to interpret structure-preserving neural network
approximations of the regularized entropy closure as a two-stage approximation
to the original entropy closure. We conduct a numerical analysis of this
approximation and investigate optimal parameter choices. Our numerical
experiments demonstrate that the method has a much lower memory footprint than
traditional methods with competitive computation times and simulation accuracy.
