Abstract
The Chilean coast is a very seismically active region. In the 21st century, the Chilean region experienced 19 earthquakes with a magnitude of 6.2 to 8.8, where 597 people were killed. The most dangerous earthquakes occur at the bottom of the ocean. The tsunamis they cause are very dangerous for residents of the surrounding coasts. In 2010, as many as 525 people died in a destructive tsunami caused by an underwater earthquake. Our research paper aims to develop a tsunami simulator based on the modern methodology of Physics Informed Neural Networks (PINN). We test our model using a tsunami caused by a hypothetical earthquake off the coast of the densely populated area of Valparaiso, Chile. We employ a longest-edge refinement algorithm expressed by graph transformation rules to generate a sequence of triangular computational meshes approximating the seabed and seashore of the Valparaiso area based on the Global Multi-Resolution Topography Data available. For the training of the PINN, we employ points from the vertices of the generated triangular mesh.
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Acknowledgements
The work of Albert Oliver Serra was supported by “Ayudas para la recualificación del sistema universitario español” grant funded by the ULPGC, the Ministry of Universities by Order UNI/501/2021 of 26 May, and the European Union-Next Generation EU Funds The authors are grateful for support from the funds the Polish Ministry of Science and Higher Education assigned to AGH University of Krakow. The visit of Maciej Paszyński at Oden Institute was supported by J. T. Oden Research Faculty Fellowship.
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Niewiadomska, A. et al. (2024). Modeling Tsunami Waves at the Coastline of Valparaiso Area of Chile with Physics Informed Neural Networks. In: Franco, L., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds) Computational Science – ICCS 2024. ICCS 2024. Lecture Notes in Computer Science, vol 14833. Springer, Cham. https://doi.org/10.1007/978-3-031-63751-3_14
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