Physics-informed neural networks for tsunami inundation modeling

TL;DR

This paper introduces physics-informed neural networks and deep operator networks to efficiently model tsunami propagation and inundation, offering a meshless, adaptable, and faster alternative to traditional numerical methods.

Contribution

The paper presents the novel application of physics-informed neural networks and deep operator networks for tsunami modeling, enabling meshless solutions and solution operator learning for faster computations.

Findings

Accurately models tsunami propagation and inundation

Deep operator networks provide significant speed-ups

Method outperforms classical numerical approaches

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.