| Physics-informed machine learning GE Karniadakis, IG Kevrekidis, L Lu, P Perdikaris, S Wang, L Yang Nature Reviews Physics 3 (6), 422-440, 2021 | 3142 | 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 | 912 | 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 | 632 | 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 | 538 | 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 | 459 | 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 | 307 | 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 | 175* | 2024 |
| Long-time integration of parametric evolution equations with physics-informed deeponets S Wang, P Perdikaris Journal of Computational Physics 475, 111855, 2023 | 95 | 2023 |
| Mitigating propagation failures in physics-informed neural networks using retain-resample-release (r3) sampling A Daw, J Bu, S Wang, P Perdikaris, A Karpatne arXiv preprint arXiv:2207.02338, 2022 | 85* | 2022 |
| Deep learning of free boundary and Stefan problems S Wang, P Perdikaris Journal of Computational Physics 428, 109914, 2021 | 84 | 2021 |
| Improved architectures and training algorithms for deep operator networks S Wang, H Wang, P Perdikaris Journal of Scientific Computing 92 (2), 35, 2022 | 67 | 2022 |
| An expert's guide to training physics-informed neural networks S Wang, S Sankaran, H Wang, P Perdikaris arXiv preprint arXiv:2308.08468, 2023 | 35 | 2023 |
| Fast PDE-constrained optimization via self-supervised operator learning S Wang, MA Bhouri, P Perdikaris arXiv preprint arXiv:2110.13297, 2021 | 21 | 2021 |
| 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 | 6 | 2023 |
| Ensemble learning for physics informed neural networks: A gradient boosting approach Z Fang, S Wang, P Perdikaris arXiv preprint arXiv:2302.13143, 2023 | 5 | 2023 |
| Random weight factorization improves the training of continuous neural representations S Wang, H Wang, JH Seidman, P Perdikaris arXiv preprint arXiv:2210.01274, 2022 | 5 | 2022 |
| 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 | 3 | 2024 |
| A dive into spectral inference networks: improved algorithms for self-supervised learning of continuous spectral representations J Wu, SF Wang, P Perdikaris Applied Mathematics and Mechanics 44 (7), 1199-1224, 2023 | 2 | 2023 |
| PirateNets: Physics-informed Deep Learning with Residual Adaptive Networks S Wang, B Li, Y Chen, P Perdikaris arXiv preprint arXiv:2402.00326, 2024 | 1 | 2024 |
| Adaptive Training Strategies for Physics-Informed Neural Networks S Wang, P Perdikaris Knowledge-Guided Machine Learning, 133-160, 2022 | | 2022 |
Paris PerdikarisAssociate Professor, University of Pennsylvania || Principal Researcher, MSR AI4ScienceVerified email at seas.upenn.edu
