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First-principle-like reinforcement learning of nonlinear numerical schemes for conservation laws

By Hao-Chen Wang and others at
LogoUniversity of Maryland, Baltimore County
and
LogoUniversity of Stuttgart
In this study, we present a universal nonlinear numerical scheme design method enabled by multi-agent reinforcement learning (MARL). Different from contemporary supervised-learning-based and reinforcement-learning-based approaches, no reference data and special numerical treatments are used in the MARL-based method developed here; instead, a first-principle-like approach using fundamental computational fluid dynamics (CFD)... Show more
December 20, 2023
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