613 citations · 1.5k across the 10 of their papers we have counts for
4 papers · 1 filter
PhyCRNet: Physics-informed Convolutional-Recurrent Network for Solving Spatiotemporal PDEs
Pu Ren, Chengping Rao, Yang Liu +2
Partial differential equations (PDEs) play a fundamental role in modeling and simulating problems across a wide range of disciplines. Recent advances in deep learning have shown th…
Uncovering Closed-form Governing Equations of Nonlinear Dynamics from Videos
Lele Luan, Yang Liu, Hao Sun
Distilling analytical models from data has the potential to advance our understanding and prediction of nonlinear dynamics. Although discovery of governing equations based on obser…
Physics-informed Spline Learning for Nonlinear Dynamics Discovery
Fangzheng Sun, Yang Liu, Hao Sun
Dynamical systems are typically governed by a set of linear/nonlinear differential equations. Distilling the analytical form of these equations from very limited data remains intra…
Hard Encoding of Physics for Learning Spatiotemporal Dynamics
Chengping Rao, Hao Sun, Yang Liu
Modeling nonlinear spatiotemporal dynamical systems has primarily relied on partial differential equations (PDEs). However, the explicit formulation of PDEs for many underexplored…