Physics-informed neural networks for tsunami inundation modeling
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866910499650666496 |
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| author | Brecht, Rüdiger Cardoso-Bihlo, Elsa Bihlo, Alex |
| author_facet | Brecht, Rüdiger Cardoso-Bihlo, Elsa Bihlo, Alex |
| contents | We use physics-informed neural networks for solving the shallow-water equations for tsunami modeling. Physics-informed neural networks are an optimization based approach for solving differential equations that is completely meshless. This substantially simplifies the modeling of the inundation process of tsunamis. While physics-informed neural networks require retraining for each particular new initial condition of the shallow-water equations, we also introduce the use of deep operator networks that can be trained to learn the solution operator instead of a particular solution only and thus provides substantial speed-ups, also compared to classical numerical approaches for tsunami models. We show with several classical benchmarks that our method can model both tsunami propagation and the inundation process exceptionally well. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_16236 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Physics-informed neural networks for tsunami inundation modeling Brecht, Rüdiger Cardoso-Bihlo, Elsa Bihlo, Alex Computational Physics Atmospheric and Oceanic Physics We use physics-informed neural networks for solving the shallow-water equations for tsunami modeling. Physics-informed neural networks are an optimization based approach for solving differential equations that is completely meshless. This substantially simplifies the modeling of the inundation process of tsunamis. While physics-informed neural networks require retraining for each particular new initial condition of the shallow-water equations, we also introduce the use of deep operator networks that can be trained to learn the solution operator instead of a particular solution only and thus provides substantial speed-ups, also compared to classical numerical approaches for tsunami models. We show with several classical benchmarks that our method can model both tsunami propagation and the inundation process exceptionally well. |
| title | Physics-informed neural networks for tsunami inundation modeling |
| topic | Computational Physics Atmospheric and Oceanic Physics |
| url | https://arxiv.org/abs/2406.16236 |