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Jaideep Pathak

Showing results (1-10 of 8) with videos related to

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Physical Review Letters|January 30, 2018
Model-Free Prediction of Large Spatiotemporally Chaotic Systems from Data: A Reservoir Computing ApproachJaideep Pathak, Brian Hunt, Michelle Girvan, et al.
Chaos (Woodbury, N.Y.)|January 1, 2018
Using machine learning to replicate chaotic attractors and calculate Lyapunov exponents from dataJaideep Pathak, Zhixin Lu, Brian R Hunt, et al.
Chaos (Woodbury, N.Y.)|January 3, 2020
Using machine learning to assess short term causal dependence and infer network linksAmitava Banerjee, Jaideep Pathak, Rajarshi Roy, et al.
Chaos (Woodbury, N.Y.)|May 1, 2017
Reservoir observers: Model-free inference of unmeasured variables in chaotic systemsZhixin Lu, Jaideep Pathak, Brian Hunt, et al.
Chaos (Woodbury, N.Y.)|July 9, 2021
Using data assimilation to train a hybrid forecast system that combines machine-learning and knowledge-based componentsAlexander Wikner, Jaideep Pathak, Brian R Hunt, et al.
Chaos (Woodbury, N.Y.)|January 8, 2020
Hybrid forecasting of chaotic processes: Using machine learning in conjunction with a knowledge-based modelJaideep Pathak, Alexander Wikner, Rebeckah Fussell, et al.
Chaos (Woodbury, N.Y.)|June 4, 2020
Combining machine learning with knowledge-based modeling for scalable forecasting and subgrid-scale closure of large, complex, spatiotemporal systemsAlexander Wikner, Jaideep Pathak, Brian Hunt, et al.
Science Advances|January 30, 2026
Kilometer-scale convection-allowing model emulation using generative diffusion modelingJaideep Pathak, Yair Cohen, Piyush Garg, et al.
Pageof 1

Showing results (1-10 of 8) with videos related to

Sort By:
Pageof 1
Physical Review Letters|January 30, 2018
Model-Free Prediction of Large Spatiotemporally Chaotic Systems from Data: A Reservoir Computing ApproachJaideep Pathak, Brian Hunt, Michelle Girvan, et al.
Chaos (Woodbury, N.Y.)|January 1, 2018
Using machine learning to replicate chaotic attractors and calculate Lyapunov exponents from dataJaideep Pathak, Zhixin Lu, Brian R Hunt, et al.
Chaos (Woodbury, N.Y.)|January 3, 2020
Using machine learning to assess short term causal dependence and infer network linksAmitava Banerjee, Jaideep Pathak, Rajarshi Roy, et al.
Chaos (Woodbury, N.Y.)|May 1, 2017
Reservoir observers: Model-free inference of unmeasured variables in chaotic systemsZhixin Lu, Jaideep Pathak, Brian Hunt, et al.
Chaos (Woodbury, N.Y.)|July 9, 2021
Using data assimilation to train a hybrid forecast system that combines machine-learning and knowledge-based componentsAlexander Wikner, Jaideep Pathak, Brian R Hunt, et al.
Chaos (Woodbury, N.Y.)|January 8, 2020
Hybrid forecasting of chaotic processes: Using machine learning in conjunction with a knowledge-based modelJaideep Pathak, Alexander Wikner, Rebeckah Fussell, et al.
Chaos (Woodbury, N.Y.)|June 4, 2020
Combining machine learning with knowledge-based modeling for scalable forecasting and subgrid-scale closure of large, complex, spatiotemporal systemsAlexander Wikner, Jaideep Pathak, Brian Hunt, et al.
Science Advances|January 30, 2026
Kilometer-scale convection-allowing model emulation using generative diffusion modelingJaideep Pathak, Yair Cohen, Piyush Garg, et al.
Pageof 1