2 citations · 2 across the 1 of their papers we have counts for
7 papers
Scaling Storm-Resolving Atmospheric AI Simulation to the Entire Planet
Zeyuan Hu, Akshay Subramaniam, Noel Keen +9
Kilometer-scale convection shapes precipitation extremes, tropical organization, and cloud feedbacks, but most global atmospheric models approximate these processes at 25-100 km re…
ShardTensor: Domain Parallelism for Scientific Machine Learning
Corey Adams, Peter Harrington, Akshay Subramaniam +4
Scientific Machine Learning (SciML) faces unique challenges for extreme-resolution data, with mitigations that often fail to scale or degrade the accuracy of trained models. While…
HealDA: Highlighting the importance of initial errors in end-to-end AI weather forecasts
Aayush Gupta, Akshay Subramaniam, Michael S. Pritchard +6
AI weather models now rival leading numerical weather prediction (NWP) systems in medium-range skill. However, almost all still rely on NWP data assimilation (DA) to provide initia…
cuHPX: GPU-Accelerated Differentiable Spherical Harmonic Transforms on HEALPix Grids
Xiaopo Cheng, Akshay Subramaniam, Shixun Wu +1
HEALPix (Hierarchical Equal Area isoLatitude Pixelization) is a widely adopted spherical grid system in astrophysics, cosmology, and Earth sciences. Its equal-area, iso-latitude st…
Climate in a Bottle: Towards a Generative Foundation Model for the Kilometer-Scale Global Atmosphere
Noah D. Brenowitz, Tao Ge, Akshay Subramaniam +7
Climate modeling is reaching unprecedented resolution, producing petabytes of data. AI climate model emulators offer a path to computationally cheap analysis, enabling new scientif…
Imposing the Fundamental Dynamical Constraint of Hydrostatic Balance to Improve Global ML Weather Prediction
Akshay Subramaniam, Dale Durran, David Pruitt +2
Forecasting weather accurately and efficiently is a critical capability in our ability to adapt to climate change. Data driven approaches to this problem have enjoyed much success…