Ashesh Chattopadhyay
Scientific ML, ML theory, ML for climate, fluids, dynamical systems. Asst. Prof of Applied Math at UCSC. sites.google.com/view/ashesh6810/home
- Reposted by Ashesh Chattopadhyay🌪️⛈️ Weather #forecasting is computationally demanding—that's why #BaskinEngineering Assistant Professor @ashesh6810.bsky.social aims to use #AI to predict extreme weather using a fraction of the time, energy, and #computing power of today’s methods. Via @uofcalifornia.bsky.social: bit.ly/3ZrNDAU
- 🧵 1/10 We introduce a new theoretical framework for continuous score-based diffusion models, showing that standard DDPMs contain a built-in spectral failure mode when applied to any multi-scale, power-law physical system.https://arxiv.org/abs/2512.09572
- Reposted by Ashesh Chattopadhyay🌊 Research led by #BaskinEngineering Assistant Professor of Applied Mathematics @ashesh6810.bsky.social shows that regional ocean dynamics in the Gulf of Mexico can be better emulated with #AI models—offering new possibilities for navigation and extreme weather monitoring. Read on: bit.ly/4n0AHvj
- 🚨 New from our group! A stable AI framework for high-res regional ocean modeling-- joint work with Fujitsu Research and NC State led by @baskinengineering.bsky.social PhD students Lenny and @moeindarman.bsky.social. Now out in JGR: Machine Learning & Computation 🌊🤖 🔗 doi.org/10.1029/2025JH000851 🧵
- 🌍 Why this matters: Regional ocean models like the Gulf of Mexico are hard—complex coastlines, eddies, Loop Current, chaotic boundary forcing. Physics models = accurate but slow. ML = fast, but unstable after a few weeks. We wanted the best of both.
- 🚨 New preprint alert! “Generative Lagrangian Data Assimilation for Ocean Dynamics Under Extreme Sparsity” is live! 📄 arxiv.org/abs/2507.06479 🌊 Reconstructs high-res ocean states from just 0.1% data using #GenAI. No forward model needed. (1/5)
- A physical analysis of #OceanNet, our high-resolution regional ocean digital twins' predictions for the Loop Current led by Anna Lowe in collaboration with Michael Gray, Tianning Wu, and Ruoying He out in AMS AI for Earth systems. journals.ametsoc.org/view/journal...
- Reposted by Ashesh Chattopadhyay[Not loaded yet]
- Check out our new work in @pnas.org exploring AI weather's capabilities to predict OOD gray swans.
- We released a new pre-print (arxiv.org/abs/2504.15487) on understanding the physics of out-of-distribution generalization (and lack there-of) for turbulence modeling of ocean dynamics. Led by @moeindarman.bsky.social with @pedramh.bsky.social and Laure Zanna.
- Reposted by Ashesh ChattopadhyayLooking forward to learning about recent advances in #AI4Climate at the @apsphysics.bsky.social #GlobalPhysicsSummit meeting. Come check out the back-to-back focus sessions, "AI Applications in Weather and Climate I & II," on Tuesday from 9:00 AM to 1:30 PM! summit.aps.org/schedule/?c=...
- We have released a new pre-print on AI-based long-term regional ocean modeling and downscaling. arxiv.org/abs/2501.05058. This is work led by my PhD students Lenny and Moein with collaborators Roy He, Michael Gray, and Tianning Wu at NCSU and Subhashis Hazarika and Anthony Wong at Fujitsu Research
- If you are around at #AGU2024 and interested in ML for climate, please check out these talks from my group and collaborators. 1. Biases, instability, hallucinations in ML emulators of weather and climate. agu.confex.com/agu/agu24/me.... You can also see our paper here: arxiv.org/abs/2304.07029 1/5
- Arvind, me, and Jonah released a new pre-print on some pen and paper analysis of fundamental failure modes and old school stability analysis for neural PDEs typically used in AI for Science application. arxiv.org/abs/2411.15101. 1/n
- I am hiring for a #postdocposition for scientific ML + climate dynamics. Folks with deep learning, scientific computing skills; preferably some background in climate, please reach out! This is part of an #NSF project in collaboration with Nicole Feldl and Geoff Vallis. recruit.ucsc.edu/JPF01844
