| Model-free prediction of large spatiotemporally chaotic systems from data: A reservoir computing approach J Pathak, B Hunt, M Girvan, Z Lu, E Ott Physical review letters 120 (2), 024102, 2018 | 1112 | 2018 |
| Using machine learning to replicate chaotic attractors and calculate Lyapunov exponents from data J Pathak, Z Lu, BR Hunt, M Girvan, E Ott Chaos: An Interdisciplinary Journal of Nonlinear Science 27 (12), 2017 | 574 | 2017 |
| Fourcastnet: A global data-driven high-resolution weather model using adaptive fourier neural operators J Pathak, S Subramanian, P Harrington, S Raja, A Chattopadhyay, ... arXiv preprint arXiv:2202.11214, 2022 | 435 | 2022 |
| Backpropagation algorithms and reservoir computing in recurrent neural networks for the forecasting of complex spatiotemporal dynamics PR Vlachas, J Pathak, BR Hunt, TP Sapsis, M Girvan, E Ott, ... Neural Networks 126, 191-217, 2020 | 390 | 2020 |
| Hybrid forecasting of chaotic processes: Using machine learning in conjunction with a knowledge-based model J Pathak, A Wikner, R Fussell, S Chandra, BR Hunt, M Girvan, E Ott Chaos: An Interdisciplinary Journal of Nonlinear Science 28 (4), 2018 | 311 | 2018 |
| Reservoir observers: Model-free inference of unmeasured variables in chaotic systems Z Lu, J Pathak, B Hunt, M Girvan, R Brockett, E Ott Chaos: An Interdisciplinary Journal of Nonlinear Science 27 (4), 041102, 2017 | 302 | 2017 |
| A machine learning‐based global atmospheric forecast model T Arcomano, I Szunyogh, J Pathak, A Wikner, BR Hunt, E Ott Geophysical Research Letters 47 (9), e2020GL087776, 2020 | 124 | 2020 |
| Fourcastnet: Accelerating global high-resolution weather forecasting using adaptive fourier neural operators T Kurth, S Subramanian, P Harrington, J Pathak, M Mardani, D Hall, ... Proceedings of the platform for advanced scientific computing conference, 1-11, 2023 | 87 | 2023 |
| Combining machine learning with knowledge-based modeling for scalable forecasting and subgrid-scale closure of large, complex, spatiotemporal systems A Wikner, J Pathak, B Hunt, M Girvan, T Arcomano, I Szunyogh, ... Chaos: An Interdisciplinary Journal of Nonlinear Science 30 (5), 2020 | 75 | 2020 |
| Spherical fourier neural operators: Learning stable dynamics on the sphere B Bonev, T Kurth, C Hundt, J Pathak, M Baust, K Kashinath, ... International conference on machine learning, 2806-2823, 2023 | 60 | 2023 |
| Using machine learning to augment coarse-grid computational fluid dynamics simulations J Pathak, M Mustafa, K Kashinath, E Motheau, T Kurth, M Day arXiv preprint arXiv:2010.00072, 2020 | 48 | 2020 |
| A hybrid approach to atmospheric modeling that combines machine learning with a physics‐based numerical model T Arcomano, I Szunyogh, A Wikner, J Pathak, BR Hunt, E Ott Journal of Advances in Modeling Earth Systems 14 (3), e2021MS002712, 2022 | 46 | 2022 |
| Using data assimilation to train a hybrid forecast system that combines machine-learning and knowledge-based components A Wikner, J Pathak, BR Hunt, I Szunyogh, M Girvan, E Ott Chaos: An Interdisciplinary Journal of Nonlinear Science 31 (5), 2021 | 40 | 2021 |
| Fourcastnet: A global data-driven high-resolution weather model using adaptive fourier neural operators, arXiv J Pathak, S Subramanian, P Harrington, S Raja, A Chattopadhyay, ... arXiv preprint arXiv:2202.11214, 2022 | 37 | 2022 |
| Using machine learning to assess short term causal dependence and infer network links A Banerjee, J Pathak, R Roy, JG Restrepo, E Ott Chaos: An Interdisciplinary Journal of Nonlinear Science 29 (12), 2019 | 37 | 2019 |
| Generative residual diffusion modeling for km-scale atmospheric downscaling M Mardani, N Brenowitz, Y Cohen, J Pathak, CY Chen, CC Liu, A Vahdat, ... arXiv preprint arXiv:2309.15214, 2023 | 9 | 2023 |
| ClimSim: A large multi-scale dataset for hybrid physics-ML climate emulation S Yu, W Hannah, L Peng, J Lin, MA Bhouri, R Gupta, B Lütjens, JC Will, ... Advances in Neural Information Processing Systems 36, 2024 | 8 | 2024 |
| ClimSim: An open large-scale dataset for training high-resolution physics emulators in hybrid multi-scale climate simulators S Yu, WM Hannah, L Peng, MA Bhouri, R Gupta, J Lin, B Lütjens, JC Will, ... arXiv preprint arXiv:2306.08754, 2023 | 8 | 2023 |
| Long-term stability and generalization of observationally-constrained stochastic data-driven models for geophysical turbulence A Chattopadhyay, J Pathak, E Nabizadeh, W Bhimji, P Hassanzadeh Environmental Data Science 2, e1, 2023 | 8 | 2023 |
| Calibration of large neural weather models A Graubner, KK Azizzadenesheli, J Pathak, M Mardani, M Pritchard, ... NeurIPS 2022 Workshop on Tackling Climate Change with Machine Learning, 2022 | 4 | 2022 |
Edward OttUniversity of Maryland在 umd.edu 的电子邮件经过验证
