Nonlinear wave evolution with data-driven breaking

D. Eeltink (), H. Branger, C. Luneau, Y. He, A. Chabchoub, J. Kasparian, T. S. Bremer and T. P. Sapsis ()
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D. Eeltink: Massachusetts Institute of Technology
H. Branger: Aix-Marseille University, CNRS, Centrale Marseille, IRPHE
C. Luneau: Aix-Marseille University, CNRS, Centrale Marseille, IRPHE
Y. He: The University of Sydney
A. Chabchoub: The University of Sydney
J. Kasparian: University of Geneva
T. S. Bremer: University of Oxford
T. P. Sapsis: Massachusetts Institute of Technology

Nature Communications, 2022, vol. 13, issue 1, 1-11

Abstract: 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.

Date: 2022
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DOI: 10.1038/s41467-022-30025-z

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