Physics-informed neural networks for phase-resolved data assimilation and prediction of nonlinear ocean waves

TL;DR

This paper introduces a physics-informed neural network approach to efficiently assimilate and predict complex nonlinear ocean waves, outperforming traditional models in speed and accuracy, and capable of inferring full fluid velocity fields from surface data.

Contribution

The paper presents a novel PINN-based solver for phase-resolved wave prediction that effectively captures nonlinearity and dispersiveness, improving computational efficiency and data assimilation capabilities.

Findings

Accurately predicts nonlinear, dispersive wave dynamics.

Infers full velocity potential from surface measurements.

Validated against analytical and experimental data.

Abstract

The assimilation and prediction of phase-resolved surface gravity waves are critical challenges in ocean science and engineering. Potential flow theory (PFT) has been widely employed to develop wave models and numerical techniques for wave prediction. However, traditional wave prediction methods are often limited. For example, most simplified wave models have a limited ability to capture strong wave nonlinearity, while fully nonlinear PFT solvers often fail to meet the speed requirements of engineering applications. This computational inefficiency also hinders the development of effective data assimilation techniques, which are required to reconstruct spatial wave information from sparse measurements to initialize the wave prediction. To address these challenges, we propose a novel solver method that leverages physics-informed neural networks (PINNs) that parameterize PFT solutions as neural networks. This provides a computationally inexpensive way to assimilate and predict wave data. The proposed PINN framework is validated through comparisons with analytical linear PFT solutions and experimental data collected in a laboratory wave flume. The results demonstrate that our approach accurately captures and predicts irregular, nonlinear, and dispersive wave surface dynamics. Moreover, the PINN can infer the fully nonlinear velocity potential throughout the entire fluid volume solely from surface elevation measurements, enabling the calculation of fluid velocities that are difficult to measure experimentally.