Karthik Kashinath

35 papers A* 3A 2B 1Journal 25Unranked 4

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Examining Fast Radiative Feedbacks Using Machine-Learning Weather Emulators. CoRR Ankur Mahesh, William D. Collins, Travis A. O'Brien, Paul B. Goddard, Sinclaire Zebaze, Shashank Subramanian, James Duncan, Oliver Watt-Meyer, Boris Bonev, Thorsten Kurth, Karthik Kashinath, Michael S. Pritchard, Da Yang
2025	J	`jnl`	FourCastNet 3: A geometric approach to probabilistic machine-learning weather forecasting at scale. CoRR Boris Bonev, Thorsten Kurth, Ankur Mahesh, Mauro Bisson, Jean Kossaifi, Karthik Kashinath, Anima Anandkumar, William D. Collins, Michael S. Pritchard, Alexander Keller
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`	Huge Ensembles Part I: Design of Ensemble Weather Forecasts using Spherical Fourier Neural Operators. CoRR Ankur Mahesh, William D. Collins, Boris Bonev, Noah D. Brenowitz, Yair Cohen, Joshua Elms, Peter Harrington, Karthik Kashinath, Thorsten Kurth, Joshua North, Travis A. O'Brien, Michael S. Pritchard, David Pruitt, Mark Risser, Shashank Subramanian, Jared Willard
2024	J	`jnl`	Huge Ensembles Part II: Properties of a Huge Ensemble of Hindcasts Generated with Spherical Fourier Neural Operators. CoRR Ankur Mahesh, William D. Collins, Boris Bonev, Noah D. Brenowitz, Yair Cohen, Peter Harrington, Karthik Kashinath, Thorsten Kurth, Joshua North, Travis A. O'Brien, Michael S. Pritchard, David Pruitt, Mark Risser, Shashank Subramanian, Jared Willard
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	J	`jnl`	ACE: A fast, skillful learned global atmospheric model for climate prediction. CoRR Oliver Watt-Meyer, Gideon Dresdner, Jeremy McGibbon, Spencer K. Clark, Brian Henn, James Duncan, Noah D. Brenowitz, Karthik Kashinath, Michael S. Pritchard, Boris Bonev, Matthew E. Peters, Christopher S. Bretherton
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	J	`jnl`	Earth Virtualization Engines - A Technical Perspective. CoRR Torsten Hoefler, Bjorn Stevens, Andreas F. Prein, Johanna Baehr, Thomas C. Schulthess, Thomas F. Stocker, John A. Taylor, Daniel Klocke, Pekka Manninen, Piers M. Forster, Tobias Kölling, Nicolas Gruber, Hartwig Anzt, Claudia Frauen, Florian Ziemen, Milan Klöwer, Karthik Kashinath, Christoph M. Schär, Oliver Fuhrer, Bryan N. Lawrence
2023	J	`jnl`	Earth Virtualization Engines: A Technical Perspective. Comput. Sci. Eng. Torsten Hoefler, Bjorn Stevens, Andreas F. Prein, Johanna Baehr, Thomas C. Schulthess, Thomas F. Stocker, John A. Taylor, Daniel Klocke, Pekka Manninen, Piers M. Forster, Tobias Kölling, Nicolas Gruber, Hartwig Anzt, Claudia Frauen, Florian Ziemen, Milan Klöwer, Karthik Kashinath, Christoph M. Schär, Oliver Fuhrer, Bryan N. Lawrence
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	J	`jnl`	Physics-Informed Representation Learning for Emergent Organization in Complex Dynamical Systems. CoRR Adam Rupe, Karthik Kashinath, Nalini Kumar, James P. Crutchfield
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
2021	J	`jnl`	Towards physically consistent data-driven weather forecasting: Integrating data assimilation with equivariance-preserving deep spatial transformers. CoRR Ashesh Chattopadhyay, Mustafa Mustafa, Pedram Hassanzadeh, Eviatar Bach, Karthik Kashinath
2020	B	`conf`	Atmospheric Blocking Pattern Recognition in Global Climate Model Simulation Data. ICPR Grzegorz Muszynski, Prabhat, Jan Balewski, Karthik Kashinath, Michael F. Wehner, Vitaliy Kurlin
2020	—	`conf`	Deep spatial transformers for autoregressive data-driven forecasting of geophysical turbulence. CI Ashesh Chattopadhyay, Mustafa Mustafa, Pedram Hassanzadeh, Karthik Kashinath
2020	J	`jnl`	Enforcing statistical constraints in generative adversarial networks for modeling chaotic dynamical systems. J. Comput. Phys. Jinlong Wu, Karthik Kashinath, Adrian Albert, Dragos Chirila, Prabhat, Heng Xiao
2020	J	`jnl`	MeshfreeFlowNet: A Physics-Constrained Deep Continuous Space-Time Super-Resolution Framework. CoRR Chiyu Max Jiang, Soheil Esmaeilzadeh, Kamyar Azizzadenesheli, Karthik Kashinath, Mustafa Mustafa, Hamdi A. Tchelepi, Philip Marcus, Prabhat, Anima Anandkumar
2020	A	`conf`	MeshfreeFlowNet: a physics-constrained deep continuous space-time super-resolution framework. SC Chiyu Max Jiang, Soheil Esmaeilzadeh, Kamyar Azizzadenesheli, Karthik Kashinath, Mustafa Mustafa, Hamdi A. Tchelepi, Philip Marcus, Prabhat, Anima Anandkumar
2020	A*	`conf`	Towards Physics-informed Deep Learning for Turbulent Flow Prediction. KDD Rui Wang, Karthik Kashinath, Mustafa Mustafa, Adrian Albert, Rose Yu
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	A	`conf`	Deep-Hurricane-Tracker: Tracking and Forecasting Extreme Climate Events. WACV Sookyung Kim, Hyojin Kim, Joonseok Lee, Sangwoong Yoon, Samira Ebrahimi Kahou, Karthik Kashinath, Prabhat
2019	—	`conf`	DisCo: Physics-Based Unsupervised Discovery of Coherent Structures in Spatiotemporal Systems. MLHPC@SC Adam Rupe, Prabhat, James P. Crutchfield, Nalini Kumar, Vladislav Epifanov, Karthik Kashinath, Oleksandr Pavlyk, Frank Schlimbach, Mostofa Patwary, Sergey Maidanov, Victor W. Lee
2019	J	`jnl`	DisCo: Physics-Based Unsupervised Discovery of Coherent Structures in Spatiotemporal Systems. CoRR Adam Rupe, Nalini Kumar, Vladislav Epifanov, Karthik Kashinath, Oleksandr Pavlyk, Frank Schlimbach, Mostofa Patwary, Sergey Maidanov, Victor W. Lee, Prabhat, James P. Crutchfield
2019	—	`conf`	Spherical CNNs on Unstructured Grids. ICLR (Poster) Chiyu Max Jiang, Jingwei Huang, Karthik Kashinath, Prabhat, Philip Marcus, Matthias Nießner
2019	J	`jnl`	Spherical CNNs on Unstructured Grids. CoRR Chiyu Max Jiang, Jingwei Huang, Karthik Kashinath, Prabhat, Philip Marcus, Matthias Nießner
2019	J	`jnl`	Towards Physics-informed Deep Learning for Turbulent Flow Prediction. CoRR Rui Wang, Karthik Kashinath, Mustafa Mustafa, Adrian Albert, Rose Yu
2019	J	`jnl`	Towards Unsupervised Segmentation of Extreme Weather Events. CoRR Adam Rupe, Karthik Kashinath, Nalini Kumar, Victor W. Lee, Prabhat, James P. Crutchfield
2016	J	`jnl`	A fast and objective multidimensional kernel density estimation method: fastKDE. Comput. Stat. Data Anal. Travis A. O'Brien, Karthik Kashinath, Nicholas R. Cavanaugh, William D. Collins, John P. O'Brien