| ST-PINN: A Self-Training Physics-Informed Neural Network for Partial Differential Equations J Yan, X Chen, Z Wang, E Zhoui, J Liu 2023 International Joint Conference on Neural Networks (IJCNN), 1-8, 2023 | 8 | 2023 |
| An improved structured mesh generation method based on physics-informed neural networks X Chen, J Liu, J Yan, Z Wang, C Gong arXiv preprint arXiv:2210.09546, 2022 | 7 | 2022 |
| Evaluating mesh quality with graph neural networks Z Wang, X Chen, T Li, C Gong, Y Pang, J Liu Engineering with Computers 38 (5), 4663-4673, 2022 | 7 | 2022 |
| A Neural Network-Based Mesh Quality Indicator for Three-Dimensional Cylinder Modelling X Chen, Z Wang, J Liu, C Gong, Y Pang Entropy 24 (9), 1245, 2022 | 5 | 2022 |
| Accelerating aerodynamic design optimization based on graph convolutional neural network T Li, J Yan, X Chen, Z Wang, Q Zhang, E Zhou, C Gong, J Liu International Journal of Modern Physics C 35 (01), 2450007, 2024 | 3 | 2024 |
| Auxiliary-Tasks Learning for Physics-Informed Neural Network-Based Partial Differential Equations Solving J Yan, X Chen, Z Wang, E Zhou, J Liu arXiv preprint arXiv:2307.06167, 2023 | 2 | 2023 |
| Towards a new paradigm in intelligence-driven computational fluid dynamics simulations X Chen, Z Wang, L Deng, J Yan, C Gong, B Yang, Q Wang, Q Zhang, ... Engineering Applications of Computational Fluid Mechanics 18 (1), 2407005, 2024 | | 2024 |
| Proposing an intelligent mesh smoothing method with graph neural networks Z Wang, X Chen, J Yan, J Liu arXiv preprint arXiv:2311.12815, 2023 | | 2023 |
