| Physics-informed machine learning GE Karniadakis, IG Kevrekidis, L Lu, P Perdikaris, S Wang, L Yang Nature Reviews Physics 3 (6), 422-440, 2021 | 3156 | 2021 |
| B-PINNs: Bayesian physics-informed neural networks for forward and inverse PDE problems with noisy data L Yang, X Meng, GE Karniadakis Journal of Computational Physics 425, 109913, 2021 | 622 | 2021 |
| Physics-informed generative adversarial networks for stochastic differential equations L Yang, D Zhang, GE Karniadakis SIAM Journal on Scientific Computing 42 (1), A292-A317, 2020 | 334 | 2020 |
| Reinforcement learning for bluff body active flow control in experiments and simulations D Fan, L Yang, Z Wang, MS Triantafyllou, GE Karniadakis Proceedings of the National Academy of Sciences 117 (42), 26091-26098, 2020 | 182 | 2020 |
| Solving Inverse Stochastic Problems from Discrete Particle Observations Using the Fokker--Planck Equation and Physics-Informed Neural Networks X Chen, L Yang, J Duan, GE Karniadakis SIAM Journal on Scientific Computing 43 (3), B811-B830, 2021 | 85 | 2021 |
| Neural-net-induced Gaussian process regression for function approximation and PDE solution G Pang, L Yang, GE Karniadakis Journal of Computational Physics 384, 270-288, 2019 | 84 | 2019 |
| Highly-scalable, physics-informed GANs for learning solutions of stochastic PDEs L Yang, S Treichler, T Kurth, K Fischer, D Barajas-Solano, J Romero, ... 2019 IEEE/ACM Third Workshop on Deep Learning on Supercomputers (DLS), 1-11, 2019 | 47 | 2019 |
| Potential Flow Generator With L2 Optimal Transport Regularity for Generative Models L Yang, GE Karniadakis IEEE Transactions on Neural Networks and Learning Systems 33 (2), 528-538, 2020 | 42 | 2020 |
| Learning functional priors and posteriors from data and physics X Meng, L Yang, Z Mao, J del Águila Ferrandis, GE Karniadakis Journal of Computational Physics 457, 111073, 2022 | 40 | 2022 |
| Generative ensemble regression: Learning particle dynamics from observations of ensembles with physics-informed deep generative models L Yang, C Daskalakis, GE Karniadakis SIAM Journal on Scientific Computing 44 (1), B80-B99, 2022 | 27* | 2022 |
| In-context operator learning with data prompts for differential equation problems L Yang, S Liu, T Meng, SJ Osher Proceedings of the National Academy of Sciences 120 (39), e2310142120, 2023 | 15* | 2023 |
| Fine-Tune Language Models as Multi-Modal Differential Equation Solvers L Yang, T Meng, S Liu, SJ Osher arXiv preprint arXiv:2308.05061, 2023 | 4* | 2023 |
| PDE Generalization of In-Context Operator Networks: A Study on 1D Scalar Nonlinear Conservation Laws L Yang, SJ Osher arXiv preprint arXiv:2401.07364, 2024 | 2 | 2024 |
| Deep reinforcement learning for bluff body active flow control in experiments and simulations D Fan, L Yang, Z Wang, M Triantafyllou, G Karniadakis APS Division of Fluid Dynamics Meeting Abstracts, R01. 010, 2020 | 2 | 2020 |
| Measure-conditional discriminator with stationary optimum for GANs and statistical distance surrogates L Yang, T Meng, GE Karniadakis arXiv preprint arXiv:2101.06802, 2021 | 1 | 2021 |
| Bi-directional coupling between a PDE-domain and an adjacent Data-domain equipped with multi-fidelity sensors D Zhang, L Yang, GE Karniadakis Journal of Computational Physics 374, 121-134, 2018 | 1 | 2018 |
| Yin, Junqi 84 Zhang, Zhao 45, 69 C Adams, AA Awan, D Barajas-Solano, JK Bassett, D Bhowmik, T Bicer, ... | | |
George Em KarniadakisThe Charles Pitts Robinson and John Palmer Barstow Professor of Applied Mathematics and EngineeringVahvistettu sähköpostiosoite verkkotunnuksessa brown.edu
