96 citations · 327 across the 20 of their papers we have counts for
20 papers
Learning to Reconstruct Temperature Field from Sparse Observations with Implicit Physics Priors
Shihang Li, Zhiqiang Gong, Weien Zhou +2
Accurate reconstruction of temperature field of heat-source systems (TFR-HSS) is crucial for thermal monitoring and reliability assessment in engineering applications such as elect…
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…
Heat Source Layout Optimization Using Automatic Deep Learning Surrogate and Multimodal Neighborhood Search Algorithm
Jialiang Sun, Xiaohu Zheng, Wen Yao +3
In satellite layout design, heat source layout optimization (HSLO) is an effective technique to decrease the maximum temperature and improve the heat management of the whole system…
Consistency regularization-based Deep Polynomial Chaos Neural Network Method for Reliability Analysis
Xiaohu Zheng, Wen Yao, Yunyang Zhang +1
Polynomial chaos expansion (PCE) is a powerful surrogate model-based reliability analysis method. Generally, a PCE model with a higher expansion order is usually required to obtain…
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…
Physics-Informed Deep Monte Carlo Quantile Regression method for Interval Multilevel Bayesian Network-based Satellite Heat Reliability Analysis
Xiaohu Zheng, Wen Yao, Zhiqiang Gong +2
Temperature field reconstruction is essential for analyzing satellite heat reliability. As a representative machine learning model, the deep convolutional neural network (DCNN) is…
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…
Temperature Field Inversion of Heat-Source Systems via Physics-Informed Neural Networks
Xu Liu, Wei Peng, Zhiqiang Gong +2
Temperature field inversion of heat-source systems (TFI-HSS) with limited observations is essential to monitor the system health. Although some methods such as interpolation have b…
FCA: Learning a 3D Full-coverage Vehicle Camouflage for Multi-view Physical Adversarial Attack
Donghua Wang, Tingsong Jiang, Jialiang Sun +5
Physical adversarial attacks in object detection have attracted increasing attention. However, most previous works focus on hiding the objects from the detector by generating an in…
A novel meta-learning initialization method for physics-informed neural networks
Xu Liu, Xiaoya Zhang, Wei Peng +2
Physics-informed neural networks (PINNs) have been widely used to solve various scientific computing problems. However, large training costs limit PINNs for some real-time applicat…
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…