Jaideep Pathak

26 papers A* 3Journal 20Unranked 1

Year	Rank	Type	Title / Venue / Authors
2025	A*	`conf`	Heavy-Tailed Diffusion Models. ICLR Kushagra Pandey, Jaideep Pathak, Yilun Xu, Stephan Mandt, Michael S. Pritchard, Arash Vahdat, Morteza Mardani
2024	J	`jnl`	A Practical Probabilistic Benchmark for AI Weather Models. CoRR Noah D. Brenowitz, Yair Cohen, Jaideep Pathak, Ankur Mahesh, Boris Bonev, Thorsten Kurth, Dale R. Durran, Peter Harrington, Michael S. Pritchard
2024	J	`jnl`	Coupled Ocean-Atmosphere Dynamics in a Machine Learning Earth System Model. CoRR Chenggong Wang, Michael S. Pritchard, Noah D. Brenowitz, Yair Cohen, Boris Bonev, Thorsten Kurth, Dale R. Durran, Jaideep Pathak
2024	J	`jnl`	DiffObs: Generative Diffusion for Global Forecasting of Satellite Observations. CoRR Jason Stock, Jaideep Pathak, Yair Cohen, Mike Pritchard, Piyush Garg, Dale R. Durran, Morteza Mardani, Noah D. Brenowitz
2024	J	`jnl`	Generative Data Assimilation of Sparse Weather Station Observations at Kilometer Scales. CoRR Peter Manshausen, Yair Cohen, Jaideep Pathak, Mike Pritchard, Piyush Garg, Morteza Mardani, Karthik Kashinath, Simon Byrne, Noah D. Brenowitz
2024	J	`jnl`	Heavy-Tailed Diffusion Models. CoRR Kushagra Pandey, Jaideep Pathak, Yilun Xu, Stephan Mandt, Michael S. Pritchard, Arash Vahdat, Morteza Mardani
2024	J	`jnl`	Kilometer-Scale Convection Allowing Model Emulation using Generative Diffusion Modeling. CoRR 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
2023	A*	`conf`	ClimSim: A large multi-scale dataset for hybrid physics-ML climate emulation. NeurIPS 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, Tom Beucler, Bryce E. Harrop, Benjamin R. Hillman, Andrea M. Jenney, Savannah L. Ferretti, Nana Liu, Animashree Anandkumar, Noah D. Brenowitz, Veronika Eyring, Nicholas Geneva, Pierre Gentine, Stephan Mandt, Jaideep Pathak, Akshay Subramaniam, Carl Vondrick, Rose Yu, Laure Zanna, Tian Zheng, Ryan Abernathey, Fiaz Ahmed, David C. Bader, Pierre Baldi, Elizabeth A. Barnes, Christopher S. Bretherton, Peter M. Caldwell, Wayne Chuang, Yilun Han, Yu Huang, Fernando Iglesias-Suarez, Sanket R. Jantre, Karthik Kashinath, Marat Khairoutdinov, Thorsten Kurth, Nicholas J. Lutsko, Po-Lun Ma, Griffin Mooers, J. David Neelin, David A. Randall, Sara Shamekh, Mark Taylor, Nathan M. Urban, Janni Yuval, Guang Zhang, Mike Pritchard
2023	J	`jnl`	ClimSim: An open large-scale dataset for training high-resolution physics emulators in hybrid multi-scale climate simulators. CoRR 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, Savannah L. Ferretti, Nana Liu, Anima Anandkumar, Noah D. Brenowitz, Veronika Eyring, Pierre Gentine, Stephan Mandt, Jaideep Pathak, Carl Vondrick, Rose Yu, Laure Zanna, Ryan P. Abernathey, Fiaz Ahmed, David C. Bader, Pierre Baldi, Elizabeth A. Barnes, Gunnar Behrens, Christopher S. Bretherton, Julius J. M. Busecke, Peter M. Caldwell, Wayne Chuang, Yilun Han, Yu Huang, Fernando Iglesias-Suarez, Sanket R. Jantre, Karthik Kashinath, Marat Khairoutdinov, Thorsten Kurth, Nicholas J. Lutsko, Po-Lun Ma, Griffin Mooers, J. David Neelin, David A. Randall, Sara Shamekh, Akshay Subramaniam, Mark A. Taylor, et al.
2023	—	`conf`	FourCastNet: Accelerating Global High-Resolution Weather Forecasting Using Adaptive Fourier Neural Operators. PASC Thorsten Kurth, Shashank Subramanian, Peter Harrington, Jaideep Pathak, Morteza Mardani, David Hall, Andrea Miele, Karthik Kashinath, Anima Anandkumar
2023	J	`jnl`	Generative Residual Diffusion Modeling for Km-scale Atmospheric Downscaling. CoRR Morteza Mardani, Noah D. Brenowitz, Yair Cohen, Jaideep Pathak, Chieh-Yu Chen, Cheng-Chin Liu, Arash Vahdat, Karthik Kashinath, Jan Kautz, Mike Pritchard
2023	A*	`conf`	Spherical Fourier Neural Operators: Learning Stable Dynamics on the Sphere. ICML Boris Bonev, Thorsten Kurth, Christian Hundt, Jaideep Pathak, Maximilian Baust, Karthik Kashinath, Anima Anandkumar
2023	J	`jnl`	Spherical Fourier Neural Operators: Learning Stable Dynamics on the Sphere. CoRR Boris Bonev, Thorsten Kurth, Christian Hundt, Jaideep Pathak, Maximilian Baust, Karthik Kashinath, Anima Anandkumar
2022	J	`jnl`	DL-Corrector-Remapper: A grid-free bias-correction deep learning methodology for data-driven high-resolution global weather forecasting. CoRR Tao Ge, Jaideep Pathak, Akshay Subramaniam, Karthik Kashinath
2022	J	`jnl`	FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators. CoRR Jaideep Pathak, Shashank Subramanian, Peter Harrington, Sanjeev Raja, Ashesh Chattopadhyay, Morteza Mardani, Thorsten Kurth, David Hall, Zongyi Li, Kamyar Azizzadenesheli, Pedram Hassanzadeh, Karthik Kashinath, Animashree Anandkumar
2022	J	`jnl`	FourCastNet: Accelerating Global High-Resolution Weather Forecasting using Adaptive Fourier Neural Operators. CoRR Thorsten Kurth, Shashank Subramanian, Peter Harrington, Jaideep Pathak, Morteza Mardani, David Hall, Andrea Miele, Karthik Kashinath, Animashree Anandkumar
2022	J	`jnl`	Long-term stability and generalization of observationally-constrained stochastic data-driven models for geophysical turbulence. CoRR Ashesh Chattopadhyay, Jaideep Pathak, Ebrahim Nabizadeh, Wahid Bhimji, Pedram Hassanzadeh
2021	—	`ch.`	Reservoir Computing for Forecasting Large Spatiotemporal Dynamical Systems. Reservoir Computing Jaideep Pathak, Edward Ott
2021	J	`jnl`	Using Data Assimilation to Train a Hybrid Forecast System that Combines Machine-Learning and Knowledge-Based Components. CoRR Alexander Wikner, Jaideep Pathak, Brian R. Hunt, Istvan Szunyogh, Michelle Girvan, Edward Ott
2020	J	`jnl`	Backpropagation algorithms and Reservoir Computing in Recurrent Neural Networks for the forecasting of complex spatiotemporal dynamics. Neural Networks Pantelis-Rafail Vlachas, Jaideep Pathak, Brian R. Hunt, Themistoklis P. Sapsis, Michelle Girvan, Edward Ott, Petros Koumoutsakos
2020	J	`jnl`	Combining Machine Learning with Knowledge-Based Modeling for Scalable Forecasting and Subgrid-Scale Closure of Large, Complex, Spatiotemporal Systems. CoRR Alexander Wikner, Jaideep Pathak, Brian R. Hunt, Michelle Girvan, Troy Arcomano, Istvan Szunyogh, Andrew Pomerance, Edward Ott
2020	J	`jnl`	Using Machine Learning to Augment Coarse-Grid Computational Fluid Dynamics Simulations. CoRR Jaideep Pathak, Mustafa Mustafa, Karthik Kashinath, Emmanuel Motheau, Thorsten Kurth, Marcus Day
2019	J	`jnl`	Forecasting of Spatio-temporal Chaotic Dynamics with Recurrent Neural Networks: a comparative study of Reservoir Computing and Backpropagation Algorithms. CoRR Pantelis-Rafail Vlachas, Jaideep Pathak, Brian R. Hunt, Themistoklis P. Sapsis, Michelle Girvan, Edward Ott, Petros Koumoutsakos
2019	—	`—`	Machine Learning Approaches for Data-Driven Analysis and Forecasting of High-Dimensional Chaotic Dynamical Systems. Jaideep Pathak
2019	J	`jnl`	Using Machine Learning to Assess Short Term Causal Dependence and Infer Network Links. CoRR Amitava Banerjee, Jaideep Pathak, Rajarshi Roy, Juan G. Restrepo, Edward Ott
2018	J	`jnl`	Hybrid Forecasting of Chaotic Processes: Using Machine Learning in Conjunction with a Knowledge-Based Model. CoRR Jaideep Pathak, Alexander Wikner, Rebeckah Fussell, Sarthak Chandra, Brian R. Hunt, Michelle Girvan, Edward Ott