Rao Kotamarthi

According to our database, Rao Kotamarthi authored at least 8 papers between 2021 and 2025.

Collaborative distances:

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

2025

Diffusion-Based, Data-Assimilation-Enabled Super-Resolution of Hub-height Winds.

Xiaolong Ma, Xu Dong, Ashley Tarrant, Lei Yang, Rao Kotamarthi, Jiali Wang, Feng Yan, Rajkumar Kettimuthu

CoRR, October, 2025

Swift: An Autoregressive Consistency Model for Efficient Weather Forecasting.

Jason Stock, Troy Arcomano, Rao Kotamarthi

CoRR, September, 2025

AERIS: Argonne Earth Systems Model for Reliable and Skillful Predictions.

Väinö Hatanpää, Eugene Ku, Jason Stock, Murali Emani, Sam Foreman, Chunyong Jung, Sandeep Madireddy, Tung Nguyen, Varuni Katti Sastry, Ray A. O. Sinurat, Sam Wheeler, Huihuo Zheng, Troy Arcomano, Venkatram Vishwanath, Rao Kotamarthi

CoRR, September, 2025

2024

Scaling transformer neural networks for skillful and reliable medium-range weather forecasting.

Tung Nguyen, Rohan Shah, Hritik Bansal, Troy Arcomano, Romit Maulik, Rao Kotamarthi, Ian T. Foster, Sandeep Madireddy, Aditya Grover

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

2023

DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies.

Shuaiwen Leon Song, Bonnie Kruft, Minjia Zhang, Conglong Li, Shiyang Chen, Chengming Zhang, Masahiro Tanaka, Xiaoxia Wu, Jeff Rasley, Ammar Ahmad Awan, Connor Holmes, Martin Cai, Adam Ghanem, Zhongzhu Zhou, Yuxiong He, Pete Luferenko, Divya Kumar, Jonathan A. Weyn, Ruixiong Zhang, Sylwester Klocek, Volodymyr Vragov, Mohammed AlQuraishi, Gustaf Ahdritz, Christina Floristean, Cristina Negri, Rao Kotamarthi, Venkatram Vishwanath, Arvind Ramanathan, Sam Foreman, Kyle Hippe, Troy Arcomano, Romit Maulik, Maxim Zvyagin, Alexander Brace, Bin Zhang, Cindy Orozco Bohorquez, Austin Clyde, Bharat Kale, Danilo Perez-Rivera, Heng Ma, Carla M. Mann, Michael W. Irvin, J. Gregory Pauloski, Logan T. Ward, Valérie Hayot-Sasson, Murali Emani, Zhen Xie, Diangen Lin, Maulik Shukla, Ian T. Foster, James J. Davis, Michael E. Papka, Thomas S. Brettin, Prasanna Balaprakash, Gina Tourassi, John Gounley, Heidi A. Hanson, Thomas E. Potok, Massimiliano Lupo Pasini, Kate Evans, Dan Lu, Dalton D. Lunga, Junqi Yin, Sajal Dash, Feiyi Wang, Mallikarjun Shankar, Isaac Lyngaas, Xiao Wang, Guojing Cong, Pei Zhang, Ming Fan, Siyan Liu, Adolfy Hoisie, Shinjae Yoo, Yihui Ren, William Tang, Kyle Felker, Alexey Svyatkovskiy, Hang Liu, Ashwin M. Aji, Angela Dalton, Michael J. Schulte, Karl W. Schulz, Yuntian Deng, Weili Nie, Josh Romero, Christian Dallago, Arash Vahdat, Chaowei Xiao, Thomas Gibbs, Anima Anandkumar, Rick Stevens

CoRR, 2023

2022

A Deep Learning Approach to Probabilistic Forecasting of Weather.

Nick Rittler, Carlo Graziani, Jiali Wang, Rao Kotamarthi

CoRR, 2022

2021

Fast and accurate learned multiresolution dynamical downscaling for precipitation.

Jiali Wang, Zhengchun Liu, Ian T. Foster, Won Chang, Rajkumar Kettimuthu, Rao Kotamarthi

CoRR, 2021

Data-Driven Deep Learning Emulators for Geophysical Forecasting.

Varuni Katti Sastry, Romit Maulik, Vishwas Rao, Bethany Lusch, S. Ashwin Renganathan, Rao Kotamarthi

Proceedings of the Computational Science - ICCS 2021, 2021