  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.
