| DeepXDE: A deep learning library for solving differential equations L Lu, X Meng, Z Mao, GE Karniadakis SIAM review 63 (1), 208-228, 2021 | 1589 | 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 | 674 | 2021 |
| A composite neural network that learns from multi-fidelity data: Application to function approximation and inverse PDE problems X Meng, GE Karniadakis Journal of Computational Physics 401, 109020, 2020 | 482 | 2020 |
| PPINN: Parareal physics-informed neural network for time-dependent PDEs X Meng, Z Li, D Zhang, GE Karniadakis Computer Methods in Applied Mechanics and Engineering 370, 113250, 2020 | 420 | 2020 |
| A comprehensive and fair comparison of two neural operators (with practical extensions) based on fair data L Lu, X Meng, S Cai, Z Mao, S Goswami, Z Zhang, GE Karniadakis Computer Methods in Applied Mechanics and Engineering 393, 114778, 2022 | 321 | 2022 |
| Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems J Yu, L Lu, X Meng, GE Karniadakis Computer Methods in Applied Mechanics and Engineering 393, 114823, 2022 | 317 | 2022 |
| Uncertainty quantification in scientific machine learning: Methods, metrics, and comparisons AF Psaros, X Meng, Z Zou, L Guo, GE Karniadakis Journal of Computational Physics 477, 111902, 2023 | 192 | 2023 |
| Multi-fidelity Bayesian neural networks: Algorithms and applications X Meng, H Babaee, GE Karniadakis Journal of Computational Physics 438, 110361, 2021 | 123 | 2021 |
| Physics-informed neural networks for solving forward and inverse flow problems via the Boltzmann-BGK formulation Q Lou, X Meng, GE Karniadakis Journal of Computational Physics 447, 110676, 2021 | 118 | 2021 |
| Bayesian Physics Informed Neural Networks for real-world nonlinear dynamical systems K Linka, A Schäfer, X Meng, Z Zou, GE Karniadakis, E Kuhl Computer Methods in Applied Mechanics and Engineering 402, 115346, 2022 | 76 | 2022 |
| Multiple-relaxation-time lattice Boltzmann model for incompressible miscible flow with large viscosity ratio and high Péclet number X Meng, Z Guo Physical Review E 92 (4), 043305, 2015 | 60 | 2015 |
| 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 | 42 | 2022 |
| A localized mass-conserving lattice Boltzmann approach for non-Newtonian fluid flows L Wang, J Mi, X Meng, Z Guo Communications in Computational Physics 17 (4), 908-924, 2015 | 39 | 2015 |
| Localized lattice Boltzmann equation model for simulating miscible viscous displacement in porous media X Meng, Z Guo International Journal of Heat and Mass Transfer 100, 767-778, 2016 | 37 | 2016 |
| NeuralUQ: A comprehensive library for uncertainty quantification in neural differential equations and operators Z Zou, X Meng, AF Psaros, GE Karniadakis SIAM Review 66 (1), 161-190, 2024 | 35 | 2024 |
| Pore-scale study on reactive mixing of miscible solutions with viscous fingering in porous media T Lei, X Meng, Z Guo Computers & Fluids 155, 146-160, 2017 | 30 | 2017 |
| A fast multi-fidelity method with uncertainty quantification for complex data correlations: Application to vortex-induced vibrations of marine risers X Meng, Z Wang, D Fan, MS Triantafyllou, GE Karniadakis Computer Methods in Applied Mechanics and Engineering 386, 114212, 2021 | 26 | 2021 |
| Boundary scheme for linear heterogeneous surface reactions in the lattice Boltzmann method X Meng, Z Guo Physical Review E 94 (5), 053307, 2016 | 21 | 2016 |
| Physics-informed neural networks with residual/gradient-based adaptive sampling methods for solving partial differential equations with sharp solutions Z Mao, X Meng Applied Mathematics and Mechanics 44 (7), 1069-1084, 2023 | 17* | 2023 |
| Correcting model misspecification in physics-informed neural networks (PINNs) Z Zou, X Meng, GE Karniadakis Journal of Computational Physics 505, 112918, 2024 | 14 | 2024 |
George Em KarniadakisThe Charles Pitts Robinson and John Palmer Barstow Professor of Applied Mathematics and Engineering在 brown.edu 的电子邮件经过验证
