Karthik Kashinath

According to our database, Karthik Kashinath authored at least 22 papers between 2016 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2023

Earth Virtualization Engines: A Technical Perspective.

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

Comput. Sci. Eng., 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, Noah D. Brenowitz, Karthik Kashinath, Michael S. Pritchard, Boris Bonev, Matthew E. Peters, Christopher S. Bretherton

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

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, 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.

CoRR, 2023

Physics-Informed Representation Learning for Emergent Organization in Complex Dynamical Systems.

Adam Rupe, Karthik Kashinath, Nalini Kumar, James P. Crutchfield

CoRR, 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

Proceedings of the Platform for Advanced Scientific Computing Conference, 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, 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

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Spherical Fourier Neural Operators: Learning Stable Dynamics on the Sphere.

Boris Bonev, Thorsten Kurth, Christian Hundt, Jaideep Pathak, Maximilian Baust, Karthik Kashinath, Anima Anandkumar

Proceedings of the International Conference on Machine Learning, 2023

2022

DL-Corrector-Remapper: A grid-free bias-correction deep learning methodology for data-driven high-resolution global weather forecasting.

Tao Ge, Jaideep Pathak, Akshay Subramaniam, Karthik Kashinath

CoRR, 2022

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, Animashree Anandkumar

CoRR, 2022

2021

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

2020

Enforcing statistical constraints in generative adversarial networks for modeling chaotic dynamical systems.

Jin-Long Wu, Karthik Kashinath, Adrian Albert, Dragos Chirila, Prabhat, Heng Xiao

J. Comput. Phys., 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

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

Proceedings of the International Conference for High Performance Computing, 2020

Towards Physics-informed Deep Learning for Turbulent Flow Prediction.

Rui Wang, Karthik Kashinath, Mustafa Mustafa, Adrian Albert, Rose Yu

Proceedings of the KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2020

Atmospheric Blocking Pattern Recognition in Global Climate Model Simulation Data.

Grzegorz Muszynski, Prabhat, Jan Balewski, Karthik Kashinath, Michael F. Wehner, Vitaliy Kurlin

Proceedings of the 25th International Conference on Pattern Recognition, 2020

Deep spatial transformers for autoregressive data-driven forecasting of geophysical turbulence.

Ashesh Chattopadhyay, Mustafa Mustafa, Pedram Hassanzadeh, Karthik Kashinath

Proceedings of the CI 2020: 10th International Conference on Climate Informatics, 2020

2019

Towards Unsupervised Segmentation of Extreme Weather Events.

Adam Rupe, Karthik Kashinath, Nalini Kumar, Victor W. Lee, Prabhat, James P. Crutchfield

CoRR, 2019

Deep-Hurricane-Tracker: Tracking and Forecasting Extreme Climate Events.

Sookyung Kim, Hyojin Kim, Joonseok Lee, Sangwoong Yoon, Samira Ebrahimi Kahou, Karthik Kashinath, Prabhat

Proceedings of the IEEE Winter Conference on Applications of Computer Vision, 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

Proceedings of the 2019 IEEE/ACM Workshop on Machine Learning in High Performance Computing Environments, 2019

Spherical CNNs on Unstructured Grids.

Chiyu Max Jiang, Jingwei Huang, Karthik Kashinath, Prabhat, Philip Marcus, Matthias Nießner

Proceedings of the 7th International Conference on Learning Representations, 2019

2016

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