Structure-preserving neural networks for the regularized entropy-based closure of a linear, kinetic, radiative transport equation
- Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
- Karlsruhe Inst. of Technology (KIT) (Germany)
- Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States); Univ. of Tennessee, Knoxville, TN (United States)
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-based 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 regularized entropy-based closures. The main idea is to interpret structure-preserving neural network approximations of the regularized entropy-based closure as a two-stage approximation to the original entropy-based 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. The code and all trained networks are provided on GitHub.
- Research Organization:
- Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)
- Grant/Contract Number:
- AC05-00OR22725
- OSTI ID:
- 2586969
- Journal Information:
- Journal of Computational Physics, Journal Name: Journal of Computational Physics Vol. 533; ISSN 0021-9991
- Publisher:
- Elsevier BVCopyright Statement
- Country of Publication:
- United States
- Language:
- English
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