Ashesh Chattopadhyay and Fujitsu Research are developing actionable AI-based climate models that reduce resource-guzzling computation time from months to minutes

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

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.

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...

Researchers show the success of their technical in a critical region: the Gulf of Mexico.

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...

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 ...

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...

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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...

Recent efforts in building AI-based weather forecasting applications have recei...

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...

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