Application of Machine Learning and Convex Limiting to Subgrid Flux Modeling in the Shallow-Water Equations
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| Format: | Preprint |
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2024
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| _version_ | 1866913810532532224 |
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| author | Timofeyev, Ilya Schwarzmann, Alexey Kuzmin, Dmitri |
| author_facet | Timofeyev, Ilya Schwarzmann, Alexey Kuzmin, Dmitri |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2407_17214 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Application of Machine Learning and Convex Limiting to Subgrid Flux Modeling in the Shallow-Water Equations Timofeyev, Ilya Schwarzmann, Alexey Kuzmin, Dmitri Computational Physics Machine Learning Atmospheric and Oceanic Physics Fluid Dynamics 65M99 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. |
| title | Application of Machine Learning and Convex Limiting to Subgrid Flux Modeling in the Shallow-Water Equations |
| topic | Computational Physics Machine Learning Atmospheric and Oceanic Physics Fluid Dynamics 65M99 |
| url | https://arxiv.org/abs/2407.17214 |