613 citations · 1.5k across the 10 of their papers we have counts for
10 papers
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…
Extracting full-field subpixel structural displacements from videos via deep learning
Lele Luan, Jingwei Zheng, Yongchao Yang +2
This paper develops a deep learning framework based on convolutional neural networks (CNNs) that enable real-time extraction of full-field subpixel structural displacements from vi…
Incremental Bayesian tensor learning for structural monitoring data imputation and response forecasting
Pu Ren, Xinyu Chen, Lijun Sun +1
There has been increased interest in missing sensor data imputation, which is ubiquitous in the field of structural health monitoring (SHM) due to discontinuous sensing caused by s…