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Jaideep Pathak
Jaideep Pathak
Verified email at nvidia.com
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Cited by
Year
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
10532018
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
5392017
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
3632020
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
3282022
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
2952018
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
2912017
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
1202020
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
722020
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
612023
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
432020
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
372022
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
362023
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
362021
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
352019
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
82023
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
52023
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
42023
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
42022
DL-Corrector-Remapper: A grid-free bias-correction deep learning methodology for data-driven high-resolution global weather forecasting
T Ge, J Pathak, A Subramaniam, K Kashinath
arXiv preprint arXiv:2210.12293, 2022
32022
Ml-pde: A framework for a machine learning enhanced pde solver
J Pathak, M Mustafa, K Kashinath, E Motheau, T Kurth, M Day
APS March Meeting Abstracts 2021, J25. 002, 2021
22021
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Articles 1–20