Application of Machine Learning and Convex Limiting to Subgrid Flux Modeling in the Shallow-Water Equations

TL;DR

This paper introduces a novel approach combining machine learning with convex flux limiting to improve subgrid flux modeling in shallow-water equations, ensuring physical property preservation and accurate coarse-grid simulations.

Contribution

It presents a new method that integrates neural networks with flux limiters for property-preserving subgrid modeling in finite volume methods.

Findings

Method produces meaningful closures even outside training scenarios.

Ensures positivity and maximum principles in flux computations.

Enhances accuracy of coarse-mesh shallow-water simulations.

Abstract

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.