322 citations · 691 across the 10 of their papers we have counts for
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Physics-informed Deep Super-resolution for Spatiotemporal Data
Pu Ren, Chengping Rao, Yang Liu +4
High-fidelity simulation of complex physical systems is exorbitantly expensive and inaccessible across spatiotemporal scales. Recently, there has been an increasing interest in lev…
Discovering Nonlinear PDEs from Scarce Data with Physics-encoded Learning
Chengping Rao, Pu Ren, Yang Liu +1
There have been growing interests in leveraging experimental measurements to discover the underlying partial differential equations (PDEs) that govern complex physical phenomena. A…
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…
Encoding physics to learn reaction-diffusion processes
Chengping Rao, Pu Ren, Qi Wang +3
Modeling complex spatiotemporal dynamical systems, such as the reaction-diffusion processes, have largely relied on partial differential equations (PDEs). However, due to insuffici…
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…