Discovering Boundary Equations for Wave Breaking using Machine Learning
Tianning Tang, Yuntian Chen, Rui Cao, Wouter Mostert, Paul H. Taylor,, Mark L. McAllister, Bing Tai, Yuxiang Ma, Adrian H. Callaghan, Thomas A. A., Adcock

TL;DR
This paper uses symbolic regression to discover a new boundary equation modeling wave breaking, offering interpretable insights into the physics of the phenomenon and potential for efficient simulations.
Contribution
The work introduces a novel boundary equation for wave breaking derived via symbolic regression, bridging data-driven discovery with physical interpretability.
Findings
Discovered a new boundary equation describing wave evolution during breaking.
Revealed a decoupling between water-air interface and fluid velocities.
Provided a reduced-order model for efficient wave simulation.
Abstract
Many supervised machine learning methods have revolutionised the empirical modelling of complex systems. These empirical models, however, are usually "black boxes" and provide only limited physical explanations about the underlying systems. Instead, so-called "knowledge discovery" methods can be used to explore the governing equations that describe observed phenomena. This paper focuses on how we can use such methods to explore underlying physics and also model a commonly observed yet not fully understood phenomenon - the breaking of ocean waves. In our work, we use symbolic regression to explore the equation that describes wave-breaking evolution from a dataset of in silico waves generated using expensive numerical methods. Our work discovers a new boundary equation that provides a reduced-order description of how the surface elevation (i.e., the water-air interface) evolves forward in…
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Taxonomy
TopicsOcean Waves and Remote Sensing · Advanced Fiber Optic Sensors · Hydrological Forecasting Using AI
