Hybrid Forecasting of Chaotic Processes: Using Machine Learning in Conjunction with a Knowledge-Based Model. - Jaideep Pathak; Alexander Wikner; Rebeckah Fussell; Sarthak Chandra; Brian R. Hunt; Michelle Girvan; Edward Ott (CoRR, 2018)

Forecasting of Spatio-temporal Chaotic Dynamics with Recurrent Neural Networks: a comparative study of Reservoir Computing and Backpropagation Algorithms. - Pantelis-Rafail Vlachas; Jaideep Pathak; Brian R. Hunt; Themistoklis P. Sapsis; Michelle Girvan; Edward Ott; Petros Koumoutsakos (CoRR, 2019)

Using Machine Learning to Assess Short Term Causal Dependence and Infer Network Links. - Amitava Banerjee; Jaideep Pathak; Rajarshi Roy 004; Juan G. Restrepo; Edward Ott (CoRR, 2019)

Machine Learning Approaches for Data-Driven Analysis and Forecasting of High-Dimensional Chaotic Dynamical Systems. - Jaideep Pathak (2019)

Combining Machine Learning with Knowledge-Based Modeling for Scalable Forecasting and Subgrid-Scale Closure of Large, Complex, Spatiotemporal Systems. - Alexander Wikner; Jaideep Pathak; Brian R. Hunt; Michelle Girvan; Troy Arcomano; Istvan Szunyogh; Andrew Pomerance; Edward Ott (CoRR, 2020)

Using Machine Learning to Augment Coarse-Grid Computational Fluid Dynamics Simulations. - Jaideep Pathak; Mustafa Mustafa; Karthik Kashinath; Emmanuel Motheau; Thorsten Kurth; Marcus Day (CoRR, 2020)

Backpropagation algorithms and Reservoir Computing in Recurrent Neural Networks for the forecasting of complex spatiotemporal dynamics. - Pantelis-Rafail Vlachas; Jaideep Pathak; Brian R. Hunt; Themistoklis P. Sapsis; Michelle Girvan; Edward Ott; Petros Koumoutsakos (Neural Networks, 2020)

Using Data Assimilation to Train a Hybrid Forecast System that Combines Machine-Learning and Knowledge-Based Components. - Alexander Wikner; Jaideep Pathak; Brian R. Hunt; Istvan Szunyogh; Michelle Girvan; Edward Ott (CoRR, 2021)

Reservoir Computing for Forecasting Large Spatiotemporal Dynamical Systems. - Jaideep Pathak; Edward Ott (Reservoir Computing, 2021)

FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators. - Jaideep Pathak; Shashank Subramanian; Peter Harrington; Sanjeev Raja; Ashesh Chattopadhyay; Morteza Mardani; Thorsten Kurth; David Hall; Zongyi Li; Kamyar Azizzadenesheli; Pedram Hassanzadeh; Karthik Kashinath; et al. (CoRR, 2022)

Long-term stability and generalization of observationally-constrained stochastic data-driven models for geophysical turbulence. - Ashesh Chattopadhyay; Jaideep Pathak; Ebrahim Nabizadeh; Wahid Bhimji; Pedram Hassanzadeh (CoRR, 2022)

DL-Corrector-Remapper: A grid-free bias-correction deep learning methodology for data-driven high-resolution global weather forecasting. - Tao Ge 002; Jaideep Pathak; Akshay Subramaniam; Karthik Kashinath (CoRR, 2022)

Spherical Fourier Neural Operators: Learning Stable Dynamics on the Sphere. - Boris Bonev; Thorsten Kurth; Christian Hundt 002; Jaideep Pathak; Maximilian Baust; Karthik Kashinath; Anima Anandkumar (ICML, 2023)

ClimSim: A large multi-scale dataset for hybrid physics-ML climate emulation. - Sungduk Yu; Walter M. Hannah; Liran Peng; Jerry Lin; Mohamed Aziz Bhouri; Ritwik Gupta; Björn Lütjens; Justus C. Will; Gunnar Behrens; Julius Busecke; Nora Loose; Charles Stern; et al. (NeurIPS, 2023)

FourCastNet: Accelerating Global High-Resolution Weather Forecasting Using Adaptive Fourier Neural Operators. - Thorsten Kurth; Shashank Subramanian; Peter Harrington; Jaideep Pathak; Morteza Mardani; David Hall; Andrea Miele; Karthik Kashinath; Anima Anandkumar (PASC, 2023)

ClimSim: An open large-scale dataset for training high-resolution physics emulators in hybrid multi-scale climate simulators. - Sungduk Yu; Walter M. Hannah; Liran Peng; Mohamed Aziz Bhouri; Ritwik Gupta; Jerry Lin; Björn Lütjens; Justus C. Will; Tom Beucler; Bryce E. Harrop; Benjamin R. Hillman; Andrea M. Jenney; et al. (CoRR, 2023)

Generative Residual Diffusion Modeling for Km-scale Atmospheric Downscaling. - Morteza Mardani; Noah D. Brenowitz; Yair Cohen; Jaideep Pathak; Chieh-Yu Chen; Cheng-Chin Liu; Arash Vahdat; Karthik Kashinath; Jan Kautz; Mike Pritchard (CoRR, 2023)

A Practical Probabilistic Benchmark for AI Weather Models. - Noah D. Brenowitz; Yair Cohen; Jaideep Pathak; Ankur Mahesh; Boris Bonev; Thorsten Kurth; Dale R. Durran; Peter Harrington; Michael S. Pritchard (CoRR, 2024)

DiffObs: Generative Diffusion for Global Forecasting of Satellite Observations. - Jason Stock; Jaideep Pathak; Yair Cohen; Mike Pritchard; Piyush Garg; Dale R. Durran; Morteza Mardani; Noah D. Brenowitz (CoRR, 2024)

Coupled Ocean-Atmosphere Dynamics in a Machine Learning Earth System Model. - Chenggong Wang; Michael S. Pritchard; Noah D. Brenowitz; Yair Cohen; Boris Bonev; Thorsten Kurth; Dale R. Durran; Jaideep Pathak (CoRR, 2024)

Generative Data Assimilation of Sparse Weather Station Observations at Kilometer Scales. - Peter Manshausen; Yair Cohen; Jaideep Pathak; Mike Pritchard; Piyush Garg; Morteza Mardani; Karthik Kashinath; Simon Byrne; Noah D. Brenowitz (CoRR, 2024)

Kilometer-Scale Convection Allowing Model Emulation using Generative Diffusion Modeling. - Jaideep Pathak; Yair Cohen; Piyush Garg; Peter Harrington; Noah D. Brenowitz; Dale R. Durran; Morteza Mardani; Arash Vahdat; Shaoming Xu; Karthik Kashinath; Michael S. Pritchard (CoRR, 2024)

Heavy-Tailed Diffusion Models. - Kushagra Pandey; Jaideep Pathak; Yilun Xu; Stephan Mandt; Michael S. Pritchard; Arash Vahdat; Morteza Mardani (ICLR, 2025)