Physics-informed neural networks for phase-resolved data assimilation and prediction of nonlinear ocean waves
File(s)
Accepted version
Author(s)
Type
Journal Article
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 solver method that leverages physics-informed neural networks (PINNs) that parametrize 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.
Date Issued
2025-09-01
Date Acceptance
2025-07-18
Citation
Physical Review Fluids, 2025, 10 (9)
ISSN
2469-990X
Publisher
American Physical Society
Journal / Book Title
Physical Review Fluids
Volume
10
Issue
9
Copyright Statement
Copyright ©2025 American Physical Society. This is the author’s accepted manuscript made available under a CC-BY licence in accordance with Imperial’s Research Publications Open Access policy (www.imperial.ac.uk/oa-policy)
License URL
Publication Status
Published
Article Number
094901
Date Publish Online
2025-09-02