Publication

Constraint-aware neural networks for Riemann problems

Jan Sickmann Hesthaven
2020
Journal Articles

Abstract

Neural networks are increasingly used in complex (data-driven) simulations as surrogates or for accelerating the computation of classical surrogates. In many applications physical constraints, such as mass or energy conservation, must be satisfied to obtain reliable results. However, standard machine learning algorithms are generally not tailored to respect such constraints.

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Constraint-aware neural networks for Riemann problems

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