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Nonlinear wave evolution with data-driven breaking

Author

Listed:
  • D. Eeltink

    (Massachusetts Institute of Technology
    University of Oxford)

  • 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
    Kyoto University
    Kyoto University)

  • J. Kasparian

    (University of Geneva)

  • T. S. Bremer

    (University of Oxford
    Delft University of Technology)

  • T. P. Sapsis

    (Massachusetts Institute of Technology)

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.

Suggested Citation

  • D. Eeltink & H. Branger & C. Luneau & Y. He & A. Chabchoub & J. Kasparian & T. S. Bremer & T. P. Sapsis, 2022. "Nonlinear wave evolution with data-driven breaking," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-30025-z
    DOI: 10.1038/s41467-022-30025-z
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    References listed on IDEAS

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    1. Janni Yuval & Paul A. O’Gorman, 2020. "Stable machine-learning parameterization of subgrid processes for climate modeling at a range of resolutions," Nature Communications, Nature, vol. 11(1), pages 1-10, December.
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