Eeltink D, Branger H, Luneau C, He Y, Chabchoub A, Kasparian J, van den Bremer T S, Sapsis T P
Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, United States.
Department of Engineering Science, University of Oxford, Oxford, UK.
Nat Commun. 2022 Apr 29;13(1):2343. doi: 10.1038/s41467-022-30025-z.
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.
波浪破碎是消散风输入到海浪中并通过非线性在频谱中传递的能量的主要机制。它决定了海况的特性，并在海洋-大气相互作用、海洋污染和异常海浪中起着关键作用。由于其湍流性质，除了在简单的短时间情况下，波浪破碎的计算需求仍然过高，难以通过直接数值模拟来解决。为了克服这一挑战，我们提出了一种混合机器学习框架，其中将基于物理的深水非破碎波非线性演化模型与循环神经网络相结合，以预测破碎波的演化。我们使用波浪水槽测量而非模拟来提供训练数据，并使用长短期记忆神经网络对演化模型应用有限域校正。我们的混合机器学习框架对波浪破碎及其对波浪演化的影响给出了出色的预测，包括对外部数据的预测。
