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Journal Articles Nature Communications Year : 2022

Nonlinear wave evolution with data-driven breaking


Abstract: Wave breaking is the main mechanism that dissipates energy input into ocean waves by wind and transferred across the spectrum by nonlinearity. It determines the properties of a sea state and plays a crucial role in ocean-atmosphere interaction, ocean pollution, and rogue waves. Owing to its turbulent nature, wave breaking remains too computationally demanding to solve using direct numerical simulations except in simple, short-duration circumstances. To overcome this challenge, we present a blended machine learning framework in which a physics-based nonlinear evolution model for deep-water, non-breaking waves and a recurrent neural network are combined to predict the evolution of breaking waves. We use wave tank measurements rather than simulations to provide training data and use a long short-term memory neural network to apply a finite-domain correction to the evolution model. Our blended machine learning framework gives excellent predictions of breaking and its effects on wave evolution, including for external data.
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hal-04017254 , version 1 (07-03-2023)



D. Eeltink, Hubert Branger, C. Luneau, Y. He, A. Chabchoub, et al.. Nonlinear wave evolution with data-driven breaking. Nature Communications, 2022, Nature Comunications, 13 (1), pp.2343. ⟨10.1038/s41467-022-30025-z⟩. ⟨hal-04017254⟩
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