Physics-informed neural networks for phase-resolved data assimilation and prediction of nonlinear ocean waves
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866913651165757440 |
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| author | Ehlers, Svenja Hoffmann, Norbert Tang, Tianning Callaghan, Adrian H. Cao, Rui Padilla, Enrique M. Fang, Yuxin Stender, Merten |
| author_facet | Ehlers, Svenja Hoffmann, Norbert Tang, Tianning Callaghan, Adrian H. Cao, Rui Padilla, Enrique M. Fang, Yuxin Stender, Merten |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_08430 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Physics-informed neural networks for phase-resolved data assimilation and prediction of nonlinear ocean waves Ehlers, Svenja Hoffmann, Norbert Tang, Tianning Callaghan, Adrian H. Cao, Rui Padilla, Enrique M. Fang, Yuxin Stender, Merten Machine Learning Atmospheric and Oceanic Physics Fluid Dynamics 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. |
| title | Physics-informed neural networks for phase-resolved data assimilation and prediction of nonlinear ocean waves |
| topic | Machine Learning Atmospheric and Oceanic Physics Fluid Dynamics |
| url | https://arxiv.org/abs/2501.08430 |