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Berkeley Lab Researchers Publish Pioneering Book on Autonomous Experimentation

CSA Staff Bring Diverse Research Efforts to 2023 AGU Fall Meeting

NERSC 50th Anniversary Among CSA Highlights at SC23

AMCR’s Wehner Explores Impact of ‘Extreme Event Attribution’ on Climate Science Research

Additional anthropogenic flooding: Each hexagonal bin symbolizes the upper limit of the number of residential buildings that would not have flooded without the added impact of climate change in Harris County, Texas, during Hurricane Harvey in 2017. (Credit: Smiley, K.T., Noy, I., Wehner, M.F. et al. Social inequalities in climate change-attributed impacts of Hurricane Harvey. Nat Comm 13, 3418, 2022. https://doi.org/10.1038/s41467-022-31056-2)

Former CS Area Intern Wins Student Research Award at SC23

Cori and Perlmutter Support New Understanding of Reaction Behind Salt-Based Nuclear Reactors

A simulation snapshot of a typical structure of ZnCl3•2, showing the metal ion in red and the Cl– ions in white

Berkeley Lab Machine Learning Experts Share Exciting Scientific Developments at NeurIPS 2023

2023 Hopper Fellow Embraces the Challenges of Large-Scale Science

The Future is Bright: CAMERA Mathematics for Accelerating Scientific Discovery

Perlmutter System Played Role in Two 2023 Gordon Bell Prize Winning Projects

The M.O. of ML: Can AI Foundation Models Drive Accelerated Scientific Discovery?

With diverse training data, a versatile neural network model can predict solutions for various equations, adapt to different tasks, even when dealing with equations of varying complexity (such as those with wavy patterns or simpler forms), and scale to meet various downstream needs. Image: Subramanian, Bhimji, Harrington, Morozov, Gholami, Keutzer, Mahoney.

Berkeley Lab CS Area to Share Computing Expertise at SC23

Berkeley Lab CS Area to Share Computing Expertise at SC23
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