| Physics-informed machine learning: A survey on problems, methods and applications Z Hao, S Liu, Y Zhang, C Ying, Y Feng, H Su, J Zhu arXiv preprint arXiv:2211.08064, 2022 | 77 | 2022 |
| Gnot: A general neural operator transformer for operator learning Z Hao, Z Wang, H Su, C Ying, Y Dong, S Liu, Z Cheng, J Song, J Zhu International Conference on Machine Learning, 12556-12569, 2023 | 41 | 2023 |
| A unified hard-constraint framework for solving geometrically complex pdes S Liu, H Zhongkai, C Ying, H Su, J Zhu, Z Cheng Advances in Neural Information Processing Systems 35, 20287-20299, 2022 | 11 | 2022 |
| Pinnacle: A comprehensive benchmark of physics-informed neural networks for solving pdes Z Hao, J Yao, C Su, H Su, Z Wang, F Lu, Z Xia, Y Zhang, S Liu, L Lu, J Zhu arXiv preprint arXiv:2306.08827, 2023 | 10 | 2023 |
| Multiadam: Parameter-wise scale-invariant optimizer for multiscale training of physics-informed neural networks J Yao, C Su, Z Hao, S Liu, H Su, J Zhu International Conference on Machine Learning, 39702-39721, 2023 | 4 | 2023 |
| Nuno: A general framework for learning parametric pdes with non-uniform data S Liu, Z Hao, C Ying, H Su, Z Cheng, J Zhu International Conference on Machine Learning, 21658-21671, 2023 | 3 | 2023 |
| Preconditioning for Physics-Informed Neural Networks S Liu, C Su, J Yao, Z Hao, H Su, Y Wu, J Zhu arXiv preprint arXiv:2402.00531, 2024 | | 2024 |
Zhongkai HaoTsinghua UniversityVerified email at mails.tsinghua.edu.cn
