| 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 | 294 | 2023 |
| 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 | 126 | 2022 |
| A comprehensive and fair comparison between mlp and kan representations for differential equations and operator networks K Shukla*, JD Toscano*, Z Wang*, Z Zou*, GE Karniadakis Computer Methods in Applied Mechanics and Engineering 431, 117290, 2024 | 57 | 2024 |
| 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 | 55 | 2024 |
| L-HYDRA: Multi-head physics-informed neural networks Z Zou, GE Karniadakis arXiv preprint arXiv:2301.02152, 2023 | 45 | 2023 |
| Discovering a reaction–diffusion model for Alzheimer’s disease by combining PINNs with symbolic regression Z Zhang, Z Zou, E Kuhl, GE Karniadakis Computer Methods in Applied Mechanics and Engineering 419, 116647, 2024 | 40 | 2024 |
| Correcting model misspecification in physics-informed neural networks (PINNs) Z Zou, X Meng, GE Karniadakis Journal of Computational Physics 505, 112918, 2024 | 35 | 2024 |
| Uncertainty quantification for noisy inputs–outputs in physics-informed neural networks and neural operators Z Zou, X Meng, GE Karniadakis Computer Methods in Applied Mechanics and Engineering 433, 117479, 2025 | 22 | 2025 |
| From pinns to pikans: Recent advances in physics-informed machine learning JD Toscano, V Oommen, AJ Varghese, Z Zou, NA Daryakenari, C Wu, ... arXiv preprint arXiv:2410.13228, 2024 | 14 | 2024 |
| Leveraging multitime Hamilton–Jacobi PDEs for certain scientific machine learning problems P Chen*, T Meng*, Z Zou*, J Darbon, GE Karniadakis SIAM Journal on Scientific Computing 46 (2), C216-C248, 2024 | 12 | 2024 |
| A generative modeling framework for inferring families of biomechanical constitutive laws in data-sparse regimes M Yin*, Z Zou*, E Zhang, C Cavinato, JD Humphrey, GE Karniadakis Journal of the Mechanics and Physics of Solids 181, 105424, 2023 | 12 | 2023 |
| Leveraging viscous Hamilton–Jacobi PDEs for uncertainty quantification in scientific machine learning Z Zou, T Meng, P Chen, J Darbon, GE Karniadakis SIAM/ASA Journal on Uncertainty Quantification 12 (4), 1165-1191, 2024 | 7 | 2024 |
| Leveraging Hamilton-Jacobi PDEs with time-dependent Hamiltonians for continual scientific machine learning P Chen*, T Meng*, Z Zou*, J Darbon, GE Karniadakis 6th Annual Learning for Dynamics & Control Conference, 1-12, 2024 | 7 | 2024 |
| NeuroSEM: A hybrid framework for simulating multiphysics problems by coupling PINNs and spectral elements K Shukla*, Z Zou*, CH Chan, A Pandey, Z Wang, GE Karniadakis Computer Methods in Applied Mechanics and Engineering 433, 117498, 2025 | 6 | 2025 |
| Large scale scattering using fast solvers based on neural operators Z Zou, A Kahana, E Zhang, E Turkel, R Ranade, J Pathak, GE Karniadakis arXiv preprint arXiv:2405.12380, 2024 | 3 | 2024 |
| HJ-sampler: A Bayesian sampler for inverse problems of a stochastic process by leveraging Hamilton-Jacobi PDEs and score-based generative models T Meng*, Z Zou*, J Darbon, GE Karniadakis arXiv preprint arXiv:2409.09614, 2024 | 2 | 2024 |
| Quantification of total uncertainty in the physics-informed reconstruction of CVSim-6 physiology M De Florio, Z Zou, DE Schiavazzi, GE Karniadakis ArXiv, arXiv: 2408.07201 v1, 2024 | 1 | 2024 |
| Hybrid preconditioned iterative solvers for 3D scattering problems using neural operators trained as foundation models Y Lee, Z Zou, A Kahana, E Turkel, GE Karniadakis 2025 Spring Central Sectional Meeting, 0 | | |
George Em KarniadakisThe Charles Pitts Robinson and John Palmer Barstow Professor of Applied Mathematics and EngineeringZweryfikowany adres z brown.edu
