Nonlinear wave evolution with data-driven breaking

D. Eeltink*, H. Branger, C. Luneau, Y. He, A. Chabchoub, J. Kasparian, T.S. van den Bremer, T. P. Sapsis*

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

11 Citations (Scopus)
35 Downloads (Pure)

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.

Original languageEnglish
Article number2343
Number of pages11
JournalNature Communications
Volume13
Issue number1
DOIs
Publication statusPublished - 2022

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