Application of Machine Learning and Convex Limiting to Subgrid Flux Modeling in the Shallow-Water Equations
This work addresses property-preserving subgrid modeling for computational fluid dynamics, but it is incremental as it builds on existing flux-limited finite volume methods.
The authors tackled the problem of subgrid flux modeling in shallow-water equations by combining machine learning with convex limiting to ensure property preservation, achieving meaningful closures even in untrained scenarios.
We propose a combination of machine learning and flux limiting for property-preserving subgrid scale modeling in the context of flux-limited finite volume methods for the one-dimensional shallow-water equations. The numerical fluxes of a conservative target scheme are fitted to the coarse-mesh averages of a monotone fine-grid discretization using a neural network to parametrize the subgrid scale components. To ensure positivity preservation and the validity of local maximum principles, we use a flux limiter that constrains the intermediate states of an equivalent fluctuation form to stay in a convex admissible set. The results of our numerical studies confirm that the proposed combination of machine learning with monolithic convex limiting produces meaningful closures even in scenarios for which the network was not trained.