96 citations · 245 across the 21 of their papers we have counts for
22 papers
MTL-FNO: A Lightweight Multi-Task Fourier Neural Operator for Sparse Field Reconstruction
Siyu Ye, Shihang Li, Zhiqiang Gong +4
Efficient onboard multi-field sparse reconstruction is essential for the autonomous operation of aerospace vehicles. While existing deep learning models exhibit promise for single-…
SynergyWarpNet: Attention-Guided Cooperative Warping for Neural Portrait Animation
Shihang Li, Zhiqiang Gong, Minming Ye +2
Recent advances in neural portrait animation have demonstrated remarked potential for applications in virtual avatars, telepresence, and digital content creation. However, traditio…
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…
A novel hybrid neural network of fluid-structure interaction prediction for two cylinders in tandem arrangement
Yanfang Lyu, Yunyang Zhang, Zhiqiang Gong +3
Deep learning has shown promise in improving computing efficiency while ensuring modeling accuracy in fluid-structure interaction (FSI) analysis. However, its current capabilities…
MultiScale Spectral-Spatial Convolutional Transformer for Hyperspectral Image Classification
Zhiqiang Gong, Xian Zhou, Wen Yao
Due to the powerful ability in capturing the global information, Transformer has become an alternative architecture of CNNs for hyperspectral image classification. However, general…
Deep Intrinsic Decomposition with Adversarial Learning for Hyperspectral Image Classification
Zhiqiang Gong, Xian Zhou, Wen Yao
Convolutional neural networks (CNNs) have been demonstrated their powerful ability to extract discriminative features for hyperspectral image classification. However, general deep…
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…
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…
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…
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 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…
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…