Enhancing Low-Order Discontinuous Galerkin Methods with Neural Ordinary Differential Equations for Compressible Navier--Stokes Equations. (arXiv:2310.18897v2 [physics.flu-dyn] UPDATED)

The growing computing power over the years has enabled simulations to become
more complex and accurate. While immensely valuable for scientific discovery
and problem-solving, however, high-fidelity simulations come with significant
computational demands. As a result, it is common to run a low-fidelity model
with a subgrid-scale model to reduce the computational cost, but selecting the
appropriate subgrid-scale models and tuning them are challenging. We propose a
novel method for learning the subgrid-scale model effects when simulating
partial differential equations augmented …

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