501 citations · 639 across the 13 of their papers we have counts for
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Physics-informed Convolutional Neural Networks for Temperature Field Prediction of Heat Source Layout without Labeled Data
Xiaoyu Zhao, Zhiqiang Gong, Yunyang Zhang +2
Recently, surrogate models based on deep learning have attracted much attention for engineering analysis and optimization. As the construction of data pairs in most engineering pro…
A Machine Learning Surrogate Modeling Benchmark for Temperature Field Reconstruction of Heat-Source Systems
Xiaoqian Chen, Zhiqiang Gong, Xiaoyu Zhao +2
Temperature field reconstruction of heat source systems (TFR-HSS) with limited monitoring sensors occurred in thermal management plays an important role in real time health detecti…
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…
Self-adaptive loss balanced Physics-informed neural networks for the incompressible Navier-Stokes equations
Zixue Xiang, Wei Peng, Xiaohu Zheng +2
There have been several efforts to Physics-informed neural networks (PINNs) in the solution of the incompressible Navier-Stokes fluid. The loss function in PINNs is a weighted sum…
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…