{"refrec":{"BRefID":351723,"RR":"<b>Eeltink, D.; Branger, H.; Luneau, C.; He, Y.; Chabchoub, A.; Kasparian, J.; van den Bremer, T.S.; Sapsis, T.P.</b> (2022). Nonlinear wave evolution with data-driven breaking. <i>Nature Comm. 13(1)</i>: 2343. <a href=\"https://dx.doi.org/10.1038/s41467-022-30025-z\" target=\"_blank\">https://dx.doi.org/10.1038/s41467-022-30025-z</a>","BEntID":349429,"PublicFlag":1,"CheckedFlag":0,"wosflag":1,"vabbflag":1,"RefStringPartII":". <i>Nature Comm. 13(1)</i>: 2343. <a href=\"https://dx.doi.org/10.1038/s41467-022-30025-z\" target=\"_blank\">https://dx.doi.org/10.1038/s41467-022-30025-z</a>","DocTypID":8,"DocType":"Journal article","MarineFlag":1,"FreshFlag":0,"BrackishFlag":0,"TerrestrialFlag":0,"Authorstring":"Eeltink, D.; Branger, H.; Luneau, C.; He, Y.; Chabchoub, A.; Kasparian, J.; van den Bremer, T.S.; Sapsis, T.P.","OrigTitleTranslFlag":0,"Authorstringtrunc":"Eeltink, D. <i>et al.</i>","Englishabstract":"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. 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