Modeling Tsunami Waves at the Coastline of Valparaiso Area of Chile with Physics Informed Neural Networks

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.