| Physics-informed machine learning GE Karniadakis, IG Kevrekidis, L Lu, P Perdikaris, S Wang, L Yang Nature Reviews Physics 3 (6), 422-440, 2021 | 5211 | 2021 |
| Understanding and mitigating gradient flow pathologies in physics-informed neural networks S Wang, Y Teng, P Perdikaris SIAM Journal on Scientific Computing 43 (5), A3055-A3081, 2021 | 1398 | 2021 |
| When and why PINNs fail to train: A neural tangent kernel perspective S Wang, X Yu, P Perdikaris Journal of Computational Physics 449, 110768, 2022 | 1062 | 2022 |
| Physics-informed neural networks for heat transfer problems S Cai, Z Wang, S Wang, P Perdikaris, GE Karniadakis Journal of Heat Transfer 143 (6), 060801, 2021 | 903 | 2021 |
| Learning the solution operator of parametric partial differential equations with physics-informed DeepONets S Wang, H Wang, P Perdikaris Science Advances 7 (40), 2021 | 763 | 2021 |
| On the eigenvector bias of Fourier feature networks: From regression to solving multi-scale PDEs with physics-informed neural networks S Wang, H Wang, P Perdikaris Computer Methods in Applied Mechanics and Engineering 384, 113938, 2021 | 506 | 2021 |
| Respecting causality for training physics-informed neural networks S Wang, S Sankaran, P Perdikaris Computer Methods in Applied Mechanics and Engineering 421, 116813, 2024 | 370 | 2024 |
| Mitigating propagation failures in physics-informed neural networks using retain-resample-release (r3) sampling A Daw, J Bu, S Wang, P Perdikaris, A Karpatne Proceedings of the 40 th International Conference on Machine Learning, 2023 | 163* | 2023 |
| An expert's guide to training physics-informed neural networks S Wang, S Sankaran, H Wang, P Perdikaris arXiv preprint arXiv:2308.08468, 2023 | 154 | 2023 |
| Long-time integration of parametric evolution equations with physics-informed deeponets S Wang, P Perdikaris Journal of Computational Physics 475, 111855, 2023 | 145 | 2023 |
| Deep learning of free boundary and Stefan problems S Wang, P Perdikaris Journal of Computational Physics 428, 109914, 2021 | 114 | 2021 |
| Improved architectures and training algorithms for deep operator networks S Wang, H Wang, P Perdikaris Journal of Scientific Computing 92 (2), 35, 2022 | 113 | 2022 |
| Fast PDE-constrained optimization via self-supervised operator learning S Wang, MA Bhouri, P Perdikaris arXiv preprint arXiv:2110.13297, 2021 | 34 | 2021 |
| Piratenets: Physics-informed deep learning with residual adaptive networks S Wang, B Li, Y Chen, P Perdikaris Journal of Machine Learning Research 25 (402), 1-51, 2024 | 25 | 2024 |
| Ppdonet: Deep operator networks for fast prediction of steady-state solutions in disk–planet systems S Mao, R Dong, L Lu, KM Yi, S Wang, P Perdikaris The Astrophysical Journal Letters 950 (2), L12, 2023 | 20 | 2023 |
| Learning only on boundaries: a physics-informed neural operator for solving parametric partial differential equations in complex geometries Z Fang, S Wang, P Perdikaris Neural computation 36 (3), 475-498, 2024 | 13 | 2024 |
| Ensemble learning for physics informed neural networks: A gradient boosting approach Z Fang, S Wang, P Perdikaris ICLR 2024 Workshop on AI4DifferentialEquations In Science, 2023 | 13 | 2023 |
| Cvit: Continuous vision transformer for operator learning S Wang, JH Seidman, S Sankaran, H Wang, GJ Pappas, P Perdikaris The Thirteenth International Conference on Learning Representations, 2025 | 12* | 2025 |
| Random weight factorization improves the training of continuous neural representations S Wang, H Wang, JH Seidman, P Perdikaris arXiv preprint arXiv:2210.01274, 2022 | 10 | 2022 |
| Surrogate modeling of multi-dimensional premixed and non-premixed combustion using pseudo-time stepping physics-informed neural networks Z Cao, K Liu, K Luo, S Wang, L Jiang, J Fan Physics of Fluids 36 (11), 2024 | 3 | 2024 |
Paris PerdikarisUniversity of PennsylvaniaE-mail confirmado em seas.upenn.edu