| Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators L Lu, P Jin, G Pang, Z Zhang, GE Karniadakis Nature machine intelligence 3 (3), 218-229, 2021 | 4572* | 2021 |
| SympNets: Intrinsic structure-preserving symplectic networks for identifying Hamiltonian systems P Jin, Z Zhang, A Zhu, Y Tang, GE Karniadakis Neural Networks 132, 166-179, 2020 | 303 | 2020 |
| MIONet: Learning multiple-input operators via tensor product P Jin, S Meng, L Lu SIAM Journal on Scientific Computing 44 (6), A3490-A3514, 2022 | 291 | 2022 |
| Quantifying the generalization error in deep learning in terms of data distribution and neural network smoothness P Jin, L Lu, Y Tang, GE Karniadakis Neural Networks 130, 85-99, 2020 | 95 | 2020 |
| Learning Poisson systems and trajectories of autonomous systems via Poisson neural networks P Jin, Z Zhang, IG Kevrekidis, GE Karniadakis IEEE Transactions on Neural Networks and Learning Systems 34 (11), 8271-8283, 2022 | 86 | 2022 |
| On numerical integration in neural ordinary differential equations A Zhu, P Jin, B Zhu, Y Tang International Conference on Machine Learning, 27527-27547, 2022 | 48 | 2022 |
| Deep Hamiltonian networks based on symplectic integrators A Zhu, P Jin, Y Tang arXiv preprint arXiv:2004.13830, 2020 | 44 | 2020 |
| Tensor neural network and its numerical integration Y Wang, P Jin, H Xie arXiv preprint arXiv:2207.02754, 2022 | 40 | 2022 |
| A hybrid iterative method based on MIONet for PDEs: Theory and numerical examples J Hu, P Jin Mathematics of Computation, 2025 | 16 | 2025 |
| Optimal unit triangular factorization of symplectic matrices P Jin, Z Lin, B Xiao Linear Algebra and its Applications 650, 236-247, 2022 | 13 | 2022 |
| Inverse modified differential equations for discovery of dynamics A Zhu, P Jin, B Zhu, Y Tang arXiv preprint arXiv:2009.01058, 2020 | 11 | 2020 |
| Unit triangular factorization of the matrix symplectic group P Jin, Y Tang, A Zhu SIAM Journal on Matrix Analysis and Applications 41 (4), 1630-1650, 2020 | 11 | 2020 |
| Approximation capabilities of measure-preserving neural networks A Zhu, P Jin, Y Tang Neural Networks 147, 72-80, 2022 | 10 | 2022 |
| Learning nonlinear operators via DeepONet L Lu, P Jin, GE Karniadakis Nature Machine Intelligence 3 (3), 218-229, 2021 | 10 | 2021 |
| Learning solution operators of PDEs defined on varying domains via MIONet S Xiao, P Jin, Y Tang arXiv preprint arXiv:2402.15097, 2024 | 7 | 2024 |
| Two-hidden-layer ReLU neural networks and finite elements P Jin Neural Networks, 108559, 2026 | 2* | 2026 |
| A deformation-based framework for learning solution mappings of PDEs defined on varying domains S Xiao, P Jin, Y Tang arXiv preprint arXiv:2412.01379, 2024 | 2 | 2024 |
| Experimental observation on a low-rank tensor model for eigenvalue problems J Hu, P Jin arXiv preprint arXiv:2302.00538, 2023 | 2 | 2023 |
| Structure preserving neural networks and applications to optimal control problems Z Zhang, P Jin, GE Karniadakis Fall Eastern Sectional Meeting. AMS, 2022 | 1 | 2022 |
| Manifold Function Encoder: Identifying Different Functions Defined on Different Manifolds J Hu, P Jin, W Zhang arXiv preprint arXiv:2512.20227, 2025 | | 2025 |
George Em KarniadakisThe Charles Pitts Robinson and John Palmer Barstow Professor of Applied Mathematics and EngineeringVerified email at brown.edu
