27 citations · 72 across the 6 of their papers we have counts for
6 papers
IDRLnet: A Physics-Informed Neural Network Library
Wei Peng, Jun Zhang, Weien Zhou +3
Physics Informed Neural Network (PINN) is a scientific computing framework used to solve both forward and inverse problems modeled by Partial Differential Equations (PDEs). This pa…
Deep Adaptive Arbitrary Polynomial Chaos Expansion: A Mini-data-driven Semi-supervised Method for Uncertainty Quantification
Wen Yao, Xiaohu Zheng, Jun Zhang +2
The surrogate model-based uncertainty quantification method has drawn much attention in many engineering fields. Polynomial chaos expansion (PCE) and deep learning (DL) are powerfu…
RBUE: A ReLU-Based Uncertainty Estimation Method of Deep Neural Networks
Yufeng Xia, Jun Zhang, Zhiqiang Gong +2
Deep neural networks (DNNs) have successfully learned useful data representations in various tasks. However, assessing the reliability of these representations remains a challenge.…
Joint Deep Reversible Regression Model and Physics-Informed Unsupervised Learning for Temperature Field Reconstruction
Zhiqiang Gong, Weien Zhou, Jun Zhang +2
Temperature monitoring during the life time of heat source components in engineering systems becomes essential to guarantee the normal work and the working life of these components…
A Deep Neural Network Surrogate Modeling Benchmark for Temperature Field Prediction of Heat Source Layout
Xianqi Chen, Xiaoyu Zhao, Zhiqiang Gong +4
Thermal issue is of great importance during layout design of heat source components in systems engineering, especially for high functional-density products. Thermal analysis genera…
Accelerating Physics-Informed Neural Network Training with Prior Dictionaries
Wei Peng, Weien Zhou, Jun Zhang +1
Physics-Informed Neural Networks (PINNs) can be regarded as general-purpose PDE solvers, but it might be slow to train PINNs on particular problems, and there is no theoretical gua…