Inference of water waves surface elevation from horizontal velocity components using physics informed neural networks (PINN)

TL;DR

This paper introduces a physics-informed neural network approach to accurately infer water surface elevation from horizontal velocity data, validated with CFD simulations of Kelvin waves, and enhances learning with Fourier Features to capture diverse frequency components.

Contribution

The paper presents a novel PINN framework incorporating Fourier Features to improve high and low frequency learning in water wave surface inference from velocity data.

Findings

PINN accurately infers wave surface elevation from velocity components.

Fourier Features improve neural network learning of spectral components.

Model successfully validated with CFD-generated Kelvin waves.

Abstract

In this paper, a mathematical model is presented to infer the wave free surface elevation from the horizontal velocity components using Physics Informed Neural Network (PINN). PINN is a deep learning framework to solve forward and inverse Ordinary/Partial Differential Equations (ODEs/PDEs). The model is verified by measuring a numerically generated Kelvin waves downstream of a KRISO Container Ship (KCS). The KCS Kelvin waves are generated using two phase Volume of Fluid (VoF) Computational Fluid Dynamics (CFD) simulation with OpenFOAM. In addition, the paper presented the use of the Fourier Features decomposition of the Neural Network inputs to avoid the spectral bias phenomena; Spectral bias is the tendency of Neural Network to converge towards the low frequency solution faster than the high frequency one. Fourier Features decomposition layer showed an improvement for the model learning, as the model was able to learn the high and low frequency components simultaneously.