Nonlinear wave evolution with data-driven breaking
D. Eeltink (Massachusetts Institute of Technology, University of Oxford)
H. Branger (Aix Marseille Université)
C. Luneau (Aix Marseille Université)
Y. He (University of Sydney)
Amin Chabchoub (University of Sydney, Kyoto University)
J. Kasparian (University of Geneva)
T.S. van den Bremer (University of Oxford, TU Delft - Environmental Fluid Mechanics)
T. P. Sapsis (Massachusetts Institute of Technology)
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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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