| Physics-informed convolutional neural networks for temperature field prediction of heat source layout without labeled data X Zhao, Z Gong, Y Zhang, W Yao, X Chen Engineering Applications of Artificial Intelligence 117, 105516, 2023 | 106 | 2023 |
| A deep neural network surrogate modeling benchmark for temperature field prediction of heat source layout X Chen, X Zhao, Z Gong, J Zhang, W Zhou, X Chen, W Yao Science China Physics, Mechanics & Astronomy 64 (11), 1, 2021 | 47 | 2021 |
| IDRLnet: A physics-informed neural network library W Peng, J Zhang, W Zhou, X Zhao, W Yao, X Chen arXiv preprint arXiv:2107.04320, 2021 | 33 | 2021 |
| A surrogate model with data augmentation and deep transfer learning for temperature field prediction of heat source layout X Zhao, Z Gong, J Zhang, W Yao, X Chen Structural and Multidisciplinary Optimization 64 (4), 2287-2306, 2021 | 32 | 2021 |
| Multi-fidelity prediction of fluid flow based on transfer learning using Fourier neural operator Y Lyu, X Zhao, Z Gong, X Kang, W Yao Physics of Fluids 35 (7), 2023 | 31 | 2023 |
| A deep learning method based on partition modeling for reconstructing temperature field X Peng, X Li, Z Gong, X Zhao, W Yao International Journal of Thermal Sciences 182, 107802, 2022 | 31 | 2022 |
| Multi-fidelity surrogate modeling for temperature field prediction using deep convolution neural network Y Zhang, Z Gong, W Zhou, X Zhao, X Zheng, W Yao Engineering Applications of Artificial Intelligence 123, 106354, 2023 | 27 | 2023 |
| A machine learning surrogate modeling benchmark for temperature field reconstruction of heat source systems X Chen, Z Gong, X Zhao, W Zhou, W Yao Science China Information Sciences 66 (5), 152203, 2023 | 18 | 2023 |
| RecFNO: A resolution-invariant flow and heat field reconstruction method from sparse observations via Fourier neural operator X Zhao, X Chen, Z Gong, W Zhou, W Yao, Y Zhang International Journal of Thermal Sciences 195, 108619, 2024 | 16 | 2024 |
| Semi-supervised semantic segmentation with uncertainty-guided self cross supervision Y Zhang, Z Gong, X Zhao, X Zheng, W Yao Proceedings of the Asian Conference on computer vision, 4631-4647, 2022 | 12 | 2022 |
| A unified framework of deep neural networks and gappy proper orthogonal decomposition for global field reconstruction X Zhao, Z Gong, X Chen, W Yao, Y Zhang 2023 International Joint Conference on Neural Networks (IJCNN), 1-8, 2023 | 7 | 2023 |
| A hybrid method based on proper orthogonal decomposition and deep neural networks for flow and heat field reconstruction X Zhao, X Chen, Z Gong, W Yao, Y Zhang Expert Systems with Applications 247, 123137, 2024 | 5 | 2024 |
| Deep Monte Carlo Quantile Regression for Quantifying Aleatoric Uncertainty in Physics-informed Temperature Field Reconstruction X Zheng, W Yao, Z Gong, Y Zhang, X Zhao, T Jiang 2022 International Joint Conference on Neural Networks (IJCNN), 1-8, 2022 | 4 | 2022 |
| Uncertainty guided ensemble self-training for semi-supervised global field reconstruction Y Zhang, Z Gong, X Zhao, W Yao Complex & Intelligent Systems 10 (1), 469-483, 2024 | 2 | 2024 |
| Contrastive enhancement using latent prototype for few-shot segmentation X Zhao, X Chen, Z Gong, W Yao, Y Zhang, X Zheng Digital Signal Processing 144, 104282, 2024 | 2 | 2024 |
| Efficient Methods for Agile Earth Observation Satellite Scheduling X Zhao, Z Wang, J Lv, Y Chen 2019 IEEE Symposium Series on Computational Intelligence (SSCI), 3158-3164, 2019 | 1 | 2019 |
