2.1k citations · 15.7k across the 131 of their papers we have counts for
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DCEM: A deep complementary energy method for solid mechanics
Yizheng Wang, Jia Sun, Timon Rabczuk +1
In recent years, the rapid advancement of deep learning has significantly impacted various fields, particularly in solving partial differential equations (PDEs) in the realm of sol…
Multigoal-oriented dual-weighted-residual error estimation using deep neural networks
Ayan Chakraborty, Thomas Wick, Xiaoying Zhuang +1
Deep learning has shown successful application in visual recognition and certain artificial intelligence tasks. Deep learning is also considered as a powerful tool with high flexib…
Deep Autoencoder based Energy Method for the Bending, Vibration, and Buckling Analysis of Kirchhoff Plates
Xiaoying Zhuang, Hongwei Guo, Naif Alajlan +1
In this paper, we present a deep autoencoder based energy method (DAEM) for the bending, vibration and buckling analysis of Kirchhoff plates. The DAEM exploits the higher order con…
Stochastic analysis of heterogeneous porous material with modified neural architecture search (NAS) based physics-informed neural networks using transfer learning
Hongwei Guo, Xiaoying Zhuang, Timon Rabczuk
In this work, a modified neural architecture search method (NAS) based physics-informed deep learning model is presented for stochastic analysis in heterogeneous porous material. M…
Analysis of three dimensional potential problems in non-homogeneous media with physics-informed deep collocation method using material transfer learning and sensitivity analysis
Hongwei Guo, Xiaoying Zhuang, Pengwan Chen +2
In this work, we present a deep collocation method for three dimensional potential problems in nonhomogeneous media. This approach utilizes a physics informed neural network with m…
High correlated variables creator machine: Prediction of the compressive strength of concrete
Aydin Shishegaran, Hessam Varaee, Timon Rabczuk +1
In this paper, we introduce a novel hybrid model for predicting the compressive strength of concrete using ultrasonic pulse velocity (UPV) and rebound number (RN). First, 516 data…