activity
20232026
most citedHeavy-Tailed Diffusion Models

2 citations · 4 across the 4 of their papers we have counts for

collaborators

5 papers

physics.ao-ph2026

Extreme events and statistics in an AI emulator of a global storm-resolving model

Anna Kwa, W. Andre Perkins, Troy Arcomano +7

We present HiRO-ACEv2 (High Resolution Output Ai2 Climate Emulator), a two-stage framework for AI emulation of the 3~km resolution X-SHiELD global storm-resolving model (GSRM), and…

physics.ao-ph2025★ 1 cited

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…

cs.LG2024★ 2 cited

Heavy-Tailed Diffusion Models

Kushagra Pandey, Jaideep Pathak, Yilun Xu +4

Diffusion models achieve state-of-the-art generation quality across many applications, but their ability to capture rare or extreme events in heavy-tailed distributions remains unc…

cs.CV2024

Stochastic Flow Matching for Resolving Small-Scale Physics

Stathi Fotiadis, Noah Brenowitz, Tomas Geffner +4

Conditioning diffusion and flow models have proven effective for super-resolving small-scale details in natural images.However, in physical sciences such as weather, super-resolvin…

cs.LG2023★ 1 cited

Understanding and Visualizing Droplet Distributions in Simulations of Shallow Clouds

Justus C. Will, Andrea M. Jenney, Kara D. Lamb +7

Thorough analysis of local droplet-level interactions is crucial to better understand the microphysical processes in clouds and their effect on the global climate. High-accuracy si…