613 citations · 1.5k across the 10 of their papers we have counts for
5 papers · 1 filter
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…
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)…
Physics-informed learning of governing equations from scarce data
Zhao Chen, Yang Liu, Hao Sun
Harnessing data to discover the underlying governing laws or equations that describe the behavior of complex physical systems can significantly advance our modeling, simulation and…
Physics-Informed Multi-LSTM Networks for Metamodeling of Nonlinear Structures
Ruiyang Zhang, Yang Liu, Hao Sun
This paper introduces an innovative physics-informed deep learning framework for metamodeling of nonlinear structural systems with scarce data. The basic concept is to incorporate…