Thrilled to share that Ashesh Chattopadhyay (@ashesh6810.bsky.social), assistant professor of applied mathematics at #BaskinEngineering, was awarded the 2026 SIAM Activity Group on Mathematics of Planet Earth Early Career Prize! Learn more: bit.ly/4wlKxNk

🌎 #BaskinEngineering Assistant Professor of Applied Mathematics @ashesh6810.bsky.social will build #AI models to project extreme Earth-system events with the support of the highly competitive Sloan Research Fellowship. Learn more: bit.ly/40f0UND

Ashesh Chattopadhyay wins Sloan Fellowship to build advanced AI for Earth-system modeling

Assistant Professor of Applied Mathematics Ashesh Chattopadhyay will build AI models to project extreme Earth-system events.

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🌪️⛈️ 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

How UC scientists are putting AI to the test

Even as they’re pushing the boundaries of research and discovery with AI, UC scientists are asking the right questions about the transformation this technology brings.

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🧵 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

Lazy Diffusion: Mitigating spectral collapse in generative diffusion-based stable autoregressive emulation of turbulent flows

Turbulent flows posses broadband, power-law spectra in which multiscale interactions couple high-wavenumber fluctuations to large-scale dynamics. Although diffusion-based generative models offer a pri...

arxiv.org

🌊 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.

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🚨 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

🚨 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

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...

🌪️ 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

Check out our new work in @pnas.org exploring AI weather's capabilities to predict OOD gray swans.

Pedram Hassanzadeh@pedramh.bsky.social · last yr.

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...

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

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=...

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

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

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

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

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

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.