# Neural network-based, structure-preserving entropy closures for the Boltzmann moment system

@article{Schotthfer2022NeuralNS, title={Neural network-based, structure-preserving entropy closures for the Boltzmann moment system}, author={Steffen Schotth{\"o}fer and Tianbai Xiao and Martin Frank and Cory D. Hauck}, journal={ArXiv}, year={2022}, volume={abs/2201.10364} }

This work presents neural network based minimal entropy closures for the moment system of the Boltzmann equation, that preserve the inherent structure of the system of partial differential equations, such as entropy dissipation and hyperbolicity. The described method embeds convexity of the moment to entropy map in the neural network approximation to preserve the structure of the minimal entropy closure. Two techniques are used to implement the methods. The first approach approximates the map…

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