Karthik Kashinath

Publication Activity (10 Years)

Years Active: 2016-2024
Publications (10 Years): 30

Top Topics

Kernel Density Estimation

Unsupervised Segmentation

Weather Forecasting

Top Venues

Comput. Sci. Eng.

Publications

Peter Manshausen, Yair Cohen, Jaideep Pathak, Mike Pritchard, Piyush Garg, Morteza Mardani, Karthik Kashinath, Simon Byrne, Noah D. Brenowitz
Generative Data Assimilation of Sparse Weather Station Observations at Kilometer Scales. CoRR (2024)
Boris Bonev, Thorsten Kurth, Christian Hundt, Jaideep Pathak, Maximilian Baust, Karthik Kashinath, Anima Anandkumar
Spherical Fourier Neural Operators: Learning Stable Dynamics on the Sphere. ICML (2023)
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
ClimSim: A large multi-scale dataset for hybrid physics-ML climate emulation. NeurIPS (2023)
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
Earth Virtualization Engines: A Technical Perspective. Comput. Sci. Eng. 25 (3) (2023)
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 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.
ClimSim: An open large-scale dataset for training high-resolution physics emulators in hybrid multi-scale climate simulators. CoRR (2023)
Adam Rupe, Karthik Kashinath, Nalini Kumar, James P. Crutchfield
Physics-Informed Representation Learning for Emergent Organization in Complex Dynamical Systems. CoRR (2023)
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
Earth Virtualization Engines - A Technical Perspective. CoRR (2023)
Boris Bonev, Thorsten Kurth, Christian Hundt, Jaideep Pathak, Maximilian Baust, Karthik Kashinath, Anima Anandkumar
Spherical Fourier Neural Operators: Learning Stable Dynamics on the Sphere. CoRR (2023)
Morteza Mardani, Noah D. Brenowitz, Yair Cohen, Jaideep Pathak, Chieh-Yu Chen, Cheng-Chin Liu, Arash Vahdat, Karthik Kashinath, Jan Kautz, Mike Pritchard
Generative Residual Diffusion Modeling for Km-scale Atmospheric Downscaling. CoRR (2023)
Thorsten Kurth, Shashank Subramanian, Peter Harrington, Jaideep Pathak, Morteza Mardani, David Hall, Andrea Miele, Karthik Kashinath, Anima Anandkumar
FourCastNet: Accelerating Global High-Resolution Weather Forecasting Using Adaptive Fourier Neural Operators. PASC (2023)
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
ACE: A fast, skillful learned global atmospheric model for climate prediction. CoRR (2023)
Thorsten Kurth, Shashank Subramanian, Peter Harrington, Jaideep Pathak, Morteza Mardani, David Hall, Andrea Miele, Karthik Kashinath, Animashree Anandkumar
FourCastNet: Accelerating Global High-Resolution Weather Forecasting using Adaptive Fourier Neural Operators. CoRR (2022)
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
FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators. CoRR (2022)
Tao Ge, Jaideep Pathak, Akshay Subramaniam, Karthik Kashinath
DL-Corrector-Remapper: A grid-free bias-correction deep learning methodology for data-driven high-resolution global weather forecasting. CoRR (2022)
Ashesh Chattopadhyay, Mustafa Mustafa, Pedram Hassanzadeh, Eviatar Bach, Karthik Kashinath
Towards physically consistent data-driven weather forecasting: Integrating data assimilation with equivariance-preserving deep spatial transformers. CoRR (2021)
Jin-Long Wu, Karthik Kashinath, Adrian Albert, Dragos Chirila, Prabhat, Heng Xiao
Enforcing statistical constraints in generative adversarial networks for modeling chaotic dynamical systems. J. Comput. Phys. 406 (2020)
Ashesh Chattopadhyay, Mustafa Mustafa, Pedram Hassanzadeh, Karthik Kashinath
Deep spatial transformers for autoregressive data-driven forecasting of geophysical turbulence. CI (2020)
Rui Wang, Karthik Kashinath, Mustafa Mustafa, Adrian Albert, Rose Yu
Towards Physics-informed Deep Learning for Turbulent Flow Prediction. KDD (2020)
Jaideep Pathak, Mustafa Mustafa, Karthik Kashinath, Emmanuel Motheau, Thorsten Kurth, Marcus Day
Using Machine Learning to Augment Coarse-Grid Computational Fluid Dynamics Simulations. CoRR (2020)
Chiyu Max Jiang, Soheil Esmaeilzadeh, Kamyar Azizzadenesheli, Karthik Kashinath, Mustafa Mustafa, Hamdi A. Tchelepi, Philip Marcus, Prabhat, Anima Anandkumar
MeshfreeFlowNet: A Physics-Constrained Deep Continuous Space-Time Super-Resolution Framework. CoRR (2020)
Grzegorz Muszynski, Prabhat, Jan Balewski, Karthik Kashinath, Michael F. Wehner, Vitaliy Kurlin
Atmospheric Blocking Pattern Recognition in Global Climate Model Simulation Data. ICPR (2020)
Chiyu Max Jiang, Soheil Esmaeilzadeh, Kamyar Azizzadenesheli, Karthik Kashinath, Mustafa Mustafa, Hamdi A. Tchelepi, Philip Marcus, Prabhat, Anima Anandkumar
MeshfreeFlowNet: a physics-constrained deep continuous space-time super-resolution framework. SC (2020)
Adam Rupe, Nalini Kumar, Vladislav Epifanov, Karthik Kashinath, Oleksandr Pavlyk, Frank Schlimbach, Mostofa Patwary, Sergey Maidanov, Victor W. Lee, Prabhat, James P. Crutchfield
DisCo: Physics-Based Unsupervised Discovery of Coherent Structures in Spatiotemporal Systems. CoRR (2019)
Rui Wang, Karthik Kashinath, Mustafa Mustafa, Adrian Albert, Rose Yu
Towards Physics-informed Deep Learning for Turbulent Flow Prediction. CoRR (2019)
Chiyu Max Jiang, Jingwei Huang, Karthik Kashinath, Prabhat, Philip Marcus, Matthias Nießner
Spherical CNNs on Unstructured Grids. ICLR (Poster) (2019)
Adam Rupe, Karthik Kashinath, Nalini Kumar, Victor W. Lee, Prabhat, James P. Crutchfield
Towards Unsupervised Segmentation of Extreme Weather Events. CoRR (2019)
Adam Rupe, Prabhat, James P. Crutchfield, Nalini Kumar, Vladislav Epifanov, Karthik Kashinath, Oleksandr Pavlyk, Frank Schlimbach, Mostofa Patwary, Sergey Maidanov, Victor W. Lee
DisCo: Physics-Based Unsupervised Discovery of Coherent Structures in Spatiotemporal Systems. MLHPC@SC (2019)
Chiyu Max Jiang, Jingwei Huang, Karthik Kashinath, Prabhat, Philip Marcus, Matthias Nießner
Spherical CNNs on Unstructured Grids. CoRR (2019)
Sookyung Kim, Hyojin Kim, Joonseok Lee, Sangwoong Yoon, Samira Ebrahimi Kahou, Karthik Kashinath, Prabhat
Deep-Hurricane-Tracker: Tracking and Forecasting Extreme Climate Events. WACV (2019)
Travis A. O'Brien, Karthik Kashinath, Nicholas R. Cavanaugh, William D. Collins, John P. O'Brien
A fast and objective multidimensional kernel density estimation method: fastKDE. Comput. Stat. Data Anal. 101 (2016)