Ashesh Chattopadhyay
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ashesh6810.bsky.social
Ashesh Chattopadhyay
@ashesh6810.bsky.social
Scientific ML, ML theory, ML for climate, fluids, dynamical systems. Asst. Prof of Applied Math at UCSC. https://sites.google.com/view/ashesh6810/home
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
Regional ocean dynamics can be better emulated with AI models
Researchers show the success of their technical in a critical region: the Gulf of Mexico.
bit.ly
October 9, 2025 at 10:58 PM
🚨 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 🧵
Simultaneous Emulation and Downscaling With Physically Consistent Deep Learning‐Based Regional Ocean Emulators
An AI-based physically consistent long-term regional emulator has been developed for the Gulf of Mexico region A deterministic and stochastic downscaling model has been developed to super-resolve...
doi.org
August 20, 2025 at 4:28 PM
🚨 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)
Generative Lagrangian data assimilation for ocean dynamics under extreme sparsity
Reconstructing ocean dynamics from observational data is fundamentally limited by the sparse, irregular, and Lagrangian nature of spatial sampling, particularly in subsurface and remote regions. This ...
arxiv.org
July 10, 2025 at 1:39 AM
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...
journals.ametsoc.org
June 2, 2025 at 8:58 AM
Reposted by Ashesh Chattopadhyay
🌪️ Can #AI predict freak weather events? AI models handle daily forecasts well—but often miss rare extremes. A team including #BaskinEngineering Asst. Prof. @ashesh6810.bsky.social is exploring how adding physics-based principles could improve AI’s accuracy in extreme cases. bit.ly/3Foh7ta
May 28, 2025 at 6:37 PM
Check out our new work in @pnas.org exploring AI weather's capabilities to predict OOD gray swans.
Can AI weather models predict out-of-distribution gray swan extremes? We report @pnas.org that the answer is NO for global gray swans, YES for regional ones: AI models can't extrapolate from weaker events but can learn from similar events in other regions during training! doi.org/10.1073/pnas...
Can AI weather models predict out-of-distribution gray swan tropical cyclones? | PNAS
Predicting gray swan weather extremes, which are possible but so rare that they are absent from the training dataset, is a major concern for AI wea...
doi.org
May 21, 2025 at 8:27 PM
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.
Fourier analysis of the physics of transfer learning for data-driven subgrid-scale models of ocean turbulence
Transfer learning (TL) is a powerful tool for enhancing the performance of neural networks (NNs) in applications such as weather and climate prediction and turbulence modeling. TL enables models to ge...
arxiv.org
April 23, 2025 at 8:21 PM
Reposted by Ashesh Chattopadhyay
Looking 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=...
March 17, 2025 at 12:24 AM
Reposted by Ashesh Chattopadhyay
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
Postdoctoral Scholar - Chattopadhyay Lab
University of California, Santa Cruz is hiring. Apply now!
recruit.ucsc.edu
November 19, 2024 at 11:24 PM
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
Simultaneous emulation and downscaling with physically-consistent deep learning-based regional ocean emulators
Building on top of the success in AI-based atmospheric emulation, we propose an AI-based ocean emulation and downscaling framework focusing on the high-resolution regional ocean over Gulf of Mexico. R...
arxiv.org
January 10, 2025 at 6:26 AM
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
Understanding and mitigating hallucinations in AI-based Earth system emulators: Towards seamless weather to climate models
Recent efforts in building AI-based weather forecasting applications have recei...
agu.confex.com
December 9, 2024 at 8:44 AM
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
What You See is Not What You Get: Neural Partial Differential Equations and The Illusion of Learning
Differentiable Programming for scientific machine learning (SciML) has recently seen considerable interest and success, as it directly embeds neural networks inside PDEs, often called as NeuralPDEs, d...
arxiv.org
November 25, 2024 at 8:45 PM
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
Postdoctoral Scholar - Chattopadhyay Lab
University of California, Santa Cruz is hiring. Apply now!
recruit.ucsc.edu
November 19, 2024 at 11:24 PM
Reposted by Ashesh Chattopadhyay
Sr Director position (weather forecast) at UChicago's new Human-Centered Weather Forecasting Initiative. Unique opportunity to lead an interdisciplinary team to generate AI- & physics-based forecasts, particularly to support communities most vulnerable to climate variability.
November 17, 2024 at 3:23 AM