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

doi:10.1038/s41467-022-30025-z

Nature Communications (Apr 2022)

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

Affiliations

D. Eeltink: Department of Mechanical Engineering, Massachusetts Institute of Technology
H. Branger: Aix-Marseille University, CNRS, Centrale Marseille, IRPHE
C. Luneau: Aix-Marseille University, CNRS, Centrale Marseille, IRPHE
Y. He: Centre for Wind, Waves and Water, School of Civil Engineering, The University of Sydney
A. Chabchoub: Centre for Wind, Waves and Water, School of Civil Engineering, The University of Sydney
J. Kasparian: Group of Applied Physics and Institute for Environmental Sciences, University of Geneva
T. S. van den Bremer: Department of Engineering Science, University of Oxford
T. P. Sapsis: Department of Mechanical Engineering, Massachusetts Institute of Technology

DOI: https://doi.org/10.1038/s41467-022-30025-z
Journal volume & issue: Vol. 13, no. 1
pp. 1 – 11

Abstract

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Wave breaking mechanisms relevant for modelling of ocean-atmosphere interaction and rogue waves, remain computationally challenging. The authors propose a machine learning framework for prediction of breaking and its effects on wave evolution that can be applied for forecasting of real world sea states.

Published in Nature Communications

ISSN: 2041-1723 (Online)
Publisher: Nature Portfolio
Country of publisher: United Kingdom
LCC subjects: Science
Website: https://www.nature.com/ncomms/

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