3 papers
cs.LG2025
Generative Discovery of Partial Differential Equations by Learning from Math Handbooks
Hao Xu, Yuntian Chen, Rui Cao +5
Data driven discovery of partial differential equations (PDEs) is a promising approach for uncovering the underlying laws governing complex systems. However, purely data driven tec…
cs.LG2025
Physics-informed neural networks for phase-resolved data assimilation and prediction of nonlinear ocean waves
Svenja Ehlers, Norbert Hoffmann, Tianning Tang +5
The assimilation and prediction of phase-resolved surface gravity waves are critical challenges in ocean science and engineering. Potential flow theory (PFT) has been widely employ…
physics.flu-dyn2024
Discovering Boundary Equations for Wave Breaking using Machine Learning
Tianning Tang, Yuntian Chen, Rui Cao +7
Many supervised machine learning methods have revolutionised the empirical modelling of complex systems. These empirical models, however, are usually "black boxes" and provide only…