🌎⚡ A frontier (1.5°, 15-day) ensemble weather forecast in ~3 seconds, from a probabilistic model you can train in ~1 day on a single H200 GPU?* Meet U-Cast, our new #ICML2026 paper. 🧵

🌍 Modeling chaos isn't just about predicting the next step—it's about modeling how uncertainty grows over time.🌪️ I’m thrilled to share Elucidated Rolling Diffusion Models (ERDM), accepted to #NeurIPS2025! We unify rolling diffusion with EDM for forecasting complex systems🧵👇

Internship in our group at Mila in reinforcement learning + graphs for reducing energy use in buildings. More info and submit an application by Jan 13 here: forms.gle/TCChXnvSAHqz... Questions? Email donna.vakalis@mila.quebec with [intern!] in the subject line.

Come talk to us tomorrow at Poster session 3: Thursday 11am-2pm at East Hall A-C #3905! (Or ping me if you'd like to chat outside of the poster session!)

UC San Diego@ucsandiego.bsky.social · 2y ago

Computer scientists at UC San Diego and the Allen Institute for AI have developed a new climate model—combining generative AI and physics data—that is capable of predicting climate patterns 25 times faster than the state of the art. ➡️ bit.ly/3ZByZbc #AI #Research #Climate

The new ACE2 climate emulator from Oliver Watt-Meyer et al has very compelling results, with results that look comparable to NeuralGCM. Congrats to the AI2 team! arxiv.org/abs/2411.112...

ACE2: Accurately learning subseasonal to decadal atmospheric variability and forced responses

Existing machine learning models of weather variability are not formulated to enable assessment of their response to varying external boundary conditions such as sea surface temperature and greenhouse...

arxiv.org

I made a starter pack for those working in or adjacent to Machine Learning for Earth System Modeling! Apologies if I forgot anyone, and feel free to suggest people to add :) go.bsky.app/C5DQNCe