Exascale deep learning for climate analytics. - Thorsten Kurth; Sean Treichler; Joshua Romero; Mayur Mudigonda; Nathan Luehr; Everett H. Phillips; Ankur Mahesh; Michael A. Matheson; Jack Deslippe; Massimiliano Fatica; Prabhat; Michael Houston (SC, 2018)

A Practical Probabilistic Benchmark for AI Weather Models. - Noah D. Brenowitz; Yair Cohen; Jaideep Pathak; Ankur Mahesh; Boris Bonev; Thorsten Kurth; Dale R. Durran; Peter Harrington; Michael S. Pritchard (CoRR, 2024)

Analyzing and Exploring Training Recipes for Large-Scale Transformer-Based Weather Prediction. - Jared D. Willard; Peter Harrington; Shashank Subramanian; Ankur Mahesh; Travis A. O'Brien; William D. Collins (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)

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)