A fast and objective multidimensional kernel density estimation method: fastKDE. - Travis A. O'Brien; Karthik Kashinath; Nicholas R. Cavanaugh; William D. Collins; John P. O'Brien (Comput. Stat. Data Anal., 2016)

Spherical CNNs on Unstructured Grids. - Chiyu Max Jiang; Jingwei Huang 001; Karthik Kashinath; Prabhat; Philip Marcus; Matthias Nießner (ICLR (Poster), 2019)

DisCo: Physics-Based Unsupervised Discovery of Coherent Structures in Spatiotemporal Systems. - Adam Rupe; Prabhat; James P. Crutchfield; Nalini Kumar; Vladislav Epifanov; Karthik Kashinath; Oleksandr Pavlyk; Frank Schlimbach; Mostofa Patwary; Sergey Maidanov; Victor W. Lee (MLHPC@SC, 2019)

Deep-Hurricane-Tracker: Tracking and Forecasting Extreme Climate Events. - Sookyung Kim; Hyojin Kim 001; Joonseok Lee; Sangwoong Yoon; Samira Ebrahimi Kahou; Karthik Kashinath; Prabhat (WACV, 2019)

Towards Unsupervised Segmentation of Extreme Weather Events. - Adam Rupe; Karthik Kashinath; Nalini Kumar; Victor W. Lee; Prabhat; James P. Crutchfield (CoRR, 2019)

Deep spatial transformers for autoregressive data-driven forecasting of geophysical turbulence. - Ashesh Chattopadhyay; Mustafa Mustafa; Pedram Hassanzadeh; Karthik Kashinath (CI, 2020)

Atmospheric Blocking Pattern Recognition in Global Climate Model Simulation Data. - Grzegorz Muszynski; Prabhat; Jan Balewski; Karthik Kashinath; Michael F. Wehner; Vitaliy Kurlin (ICPR, 2020)

Towards Physics-informed Deep Learning for Turbulent Flow Prediction. - Rui Wang 086; Karthik Kashinath; Mustafa Mustafa; Adrian Albert; Rose Yu (KDD, 2020)

MeshfreeFlowNet: a physics-constrained deep continuous space-time super-resolution framework. - Chiyu Max Jiang; Soheil Esmaeilzadeh; Kamyar Azizzadenesheli; Karthik Kashinath; Mustafa Mustafa; Hamdi A. Tchelepi; Philip Marcus; Prabhat; Anima Anandkumar (SC, 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)

Enforcing statistical constraints in generative adversarial networks for modeling chaotic dynamical systems. - Jinlong Wu; Karthik Kashinath; Adrian Albert; Dragos Chirila; Prabhat; Heng Xiao (J. Comput. Phys., 2020)

Towards physically consistent data-driven weather forecasting: Integrating data assimilation with equivariance-preserving deep spatial transformers. - Ashesh Chattopadhyay; Mustafa Mustafa; Pedram Hassanzadeh; Eviatar Bach; Karthik Kashinath (CoRR, 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)

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)

Physics-Informed Representation Learning for Emergent Organization in Complex Dynamical Systems. - Adam Rupe; Karthik Kashinath; Nalini Kumar; James P. Crutchfield (CoRR, 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)

ACE: A fast, skillful learned global atmospheric model for climate prediction. - Oliver Watt-Meyer; Gideon Dresdner; Jeremy McGibbon; Spencer K. Clark; Brian Henn; James Duncan 002; Noah D. Brenowitz; Karthik Kashinath; Michael S. Pritchard; Boris Bonev; Matthew E. Peters; Christopher S. Bretherton (CoRR, 2023)

Earth Virtualization Engines: A Technical Perspective. - Torsten Hoefler; Bjorn Stevens; Andreas F. Prein; Johanna Baehr 002; Thomas C. Schulthess; Thomas F. Stocker; John A. Taylor; Daniel Klocke; Pekka Manninen; Piers M. Forster; Tobias Kölling; Nicolas Gruber; et al. (Comput. Sci. Eng., 2023)

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)

Huge Ensembles Part II: Properties of a Huge Ensemble of Hindcasts Generated with Spherical Fourier Neural Operators. - 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; et al. (CoRR, 2024)

Huge Ensembles Part I: Design of Ensemble Weather Forecasts using Spherical Fourier Neural Operators. - 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; et al. (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)

FourCastNet 3: A geometric approach to probabilistic machine-learning weather forecasting at scale. - Boris Bonev; Thorsten Kurth; Ankur Mahesh; Mauro Bisson; Jean Kossaifi; Karthik Kashinath; Anima Anandkumar; William D. Collins; Michael S. Pritchard; Alexander Keller (CoRR, 2025)