322 citations · 691 across the 10 of their papers we have counts for
10 papers
Physics-informed neural network for seismic wave inversion in layered semi-infinite domain
Pu Ren, Chengping Rao, Hao Sun +1
Estimating the material distribution of Earth's subsurface is a challenging task in seismology and earthquake engineering. The recent development of physics-informed neural network…
SeismicNet: Physics-informed neural networks for seismic wave modeling in semi-infinite domain
Pu Ren, Chengping Rao, Su Chen +3
There has been an increasing interest in integrating physics knowledge and machine learning for modeling dynamical systems. However, very limited studies have been conducted on sei…
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…
Physics informed deep learning for computational elastodynamics without labeled data
Chengping Rao, Hao Sun, Yang Liu
Numerical methods such as finite element have been flourishing in the past decades for modeling solid mechanics problems via solving governing partial differential equations (PDEs)…
Three-dimensional convolutional neural network (3D-CNN) for heterogeneous material homogenization
Chengping Rao, Yang Liu
Homogenization is a technique commonly used in multiscale computational science and engineering for predicting collective response of heterogeneous materials and extracting effecti…
Physics-informed deep learning for incompressible laminar flows
Chengping Rao, Hao Sun, Yang Liu
Physics-informed deep learning has drawn tremendous interest in recent years to solve computational physics problems, whose basic concept is to embed physical laws to constrain/inf…