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

Liu, Yuxuan ORCID: https://orcid.org/0000-0002-4880-2374, Eeltink, Debbie ORCID: https://orcid.org/0000-0003-4560-2956, Van Den Bremer, Ton S. ORCID: https://orcid.org/0000-0001-6154-3357 and Adcock, Thomas ORCID: https://orcid.org/0000-0001-7556-1193 2024. A machine learning architecture for including wave breaking in envelope-type wave models. Ocean Engineering 305 , 118009. 10.1016/j.oceaneng.2024.118009

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Abstract

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.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Schools > Engineering
Subjects:	T Technology > TC Hydraulic engineering. Ocean engineering
Publisher:	Elsevier
ISSN:	0029-8018
Date of First Compliant Deposit:	2 April 2025
Date of Acceptance:	22 April 2024
Last Modified:	04 Apr 2025 15:49
URI:	https://orca.cardiff.ac.uk/id/eprint/177343

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