| Autor: |
D, Eeltink, H, Branger, C, Luneau, Y, He, A, Chabchoub, J, Kasparian, T S, van den Bremer, T P, Sapsis |
| Rok vydání: |
2021 |
| Zdroj: |
Nature communications. 13(1) |
| ISSN: |
2041-1723 |
| Popis: |
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. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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