501 citations · 639 across the 13 of their papers we have counts for
13 papers
Multi-fidelity prediction of fluid flow and temperature field based on transfer learning using Fourier Neural Operator
Yanfang Lyu, Xiaoyu Zhao, Zhiqiang Gong +2
Data-driven prediction of fluid flow and temperature distribution in marine and aerospace engineering has received extensive research and demonstrated its potential in real-time pr…
Uncertainty Guided Ensemble Self-Training for Semi-Supervised Global Field Reconstruction
Yunyang Zhang, Zhiqiang Gong, Xiaoyu Zhao +1
Recovering a globally accurate complex physics field from limited sensor is critical to the measurement and control in the aerospace engineering. General reconstruction methods for…
RecFNO: a resolution-invariant flow and heat field reconstruction method from sparse observations via Fourier neural operator
Xiaoyu Zhao, Xiaoqian Chen, Zhiqiang Gong +3
Perception of the full state is an essential technology to support the monitoring, analysis, and design of physical systems, one of whose challenges is to recover global field from…
Multi-fidelity surrogate modeling for temperature field prediction using deep convolution neural network
Yunyang Zhang, Zhiqiang Gong, Weien Zhou +3
Temperature field prediction is of great importance in the thermal design of systems engineering, and building the surrogate model is an effective way for the task. Generally, larg…
Semi-supervision semantic segmentation with uncertainty-guided self cross supervision
Yunyang Zhang, Zhiqiang Gong, Xiaohu Zheng +2
As a powerful way of realizing semi-supervised segmentation, the cross supervision method learns cross consistency based on independent ensemble models using abundant unlabeled ima…
Contrastive Enhancement Using Latent Prototype for Few-Shot Segmentation
Xiaoyu Zhao, Xiaoqian Chen, Zhiqiang Gong +3
Few-shot segmentation enables the model to recognize unseen classes with few annotated examples. Most existing methods adopt prototype learning architecture, where support prototyp…
Deep Monte Carlo Quantile Regression for Quantifying Aleatoric Uncertainty in Physics-informed Temperature Field Reconstruction
Xiaohu Zheng, Wen Yao, Zhiqiang Gong +3
For the temperature field reconstruction (TFR), a complex image-to-image regression problem, the convolutional neural network (CNN) is a powerful surrogate model due to the convolu…
A deep learning method based on patchwise training for reconstructing temperature field
Xingwen Peng, Xingchen Li, Zhiqiang Gong +2
Physical field reconstruction is highly desirable for the measurement and control of engineering systems. The reconstruction of the temperature field from limited observation plays…
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…