A machine learning architecture for including wave breaking in envelope-type wave models

• Published in: Ocean engineering Vol. 305; p. 118009
• Main Authors: Liu, Yuxuan, Eeltink, Debbie, van den Bremer, Ton S., Adcock, Thomas A.A.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.08.2024
• Subjects: Machine learning; Nonlinear process; Phase-resolved envelope-based wave models; Wave breaking; Phase-resolved envelope-based wave models; Wave breaking; Nonlinear process; Machine learning
• ISSN: 0029-8018; 1873-5258
• DOI: 10.1016/j.oceaneng.2024.118009

Wave breaking is a complex physical process about which open questions remain. For some applications, it is critical to include breaking effects in phase-resolved envelope-based wave models such as the non-linear Schrödinger. A promising approach is to use machine learning to capture breaking effects. In the present paper we develop the machine learning architecture to model breaking developed by Eeltink et al. (2022) further, potentially enabling more detailed breaking physics to be captured. We show that this model can be trained on focused wave groups but can also capture breaking in random waves and modulated plane waves. Analysis of the model suggests that the machine learning has broken the problem into two—one part which detects whether the wave is breaking and another which captures the subsequent behaviour, consistent with the way human scientists routinely understand the breaking problem. •Advances machine learning architecture for breaking in phase-resolved wave models.•Model tests on varied wave types, shows robust performance.•Machine Learning model splits wave breaking into detection and prediction tasks.