Fundamental research on shallow water wave propagation simulation using physics-informed neural networks

Abstract

Data-driven science is expected to serve as an alternative model to numerical simulations and has been used in the field of coastal engineering for wave prediction and tsunami simulations. However, there are challenges related to data imbalance and interpretability. In recent years, the utilization of Physics-Informed Neural Networks (PINNs), which incorporate physical laws, has advanced as a method to address these challenges. In this study, we applied PINNs to simulate a two-dimensional dam-break problem on a horizontal bed and compared the results with the numerical analysis values from the tsunami simulator T-STOC to validate the applicability of PINNs. The validation results revealed the reproducibility of PINNs, indicating that within the learned parameter range, approximate results close to the numerical analysis values could be obtained for any given parameter.