Physics-informed neural networks for tsunami inundation modeling
Abstract
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.
- Publication:
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arXiv e-prints
- Pub Date:
- June 2024
- DOI:
- 10.48550/arXiv.2406.16236
- arXiv:
- arXiv:2406.16236
- Bibcode:
- 2024arXiv240616236B
- Keywords:
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- Physics - Computational Physics;
- Physics - Atmospheric and Oceanic Physics
- E-Print:
- 16 pages, 6 figures