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Jeff Broughton Looks Back, and Prepares for the Next Thing
After 13 years attending to the nuts and bolts of NERSC, and a long career in high powered computing before that, Deputy for Operations Jeff Broughton will retire at the end of June. Read More »
Data Management Platform Strengthens Wildland Fire Science
As we face the threat of more frequent and more severe wildfires under climate change, Berkeley Lab researchers are building a managed data platform to help scientists better understand and predict wildland fire hazards, risks, and behaviors. Read More »
Simulations at NERSC Drive Progress Toward Nuclear Fusion
Simulations performed at NERSC at Berkeley Lab have brought researchers one step closer to producing efficient, endlessly renewable energy through nuclear fusion, harnessing the process that produces light and heat in our Sun and other stars. Read More »
Summer Researchers Return In-Person, Remotely
For the first time since the COVID-19 outbreak, the Computing Sciences (CS) Area’s Summer Program will host in-person participants, as well as remote. SciData Division Director Deb Agarwal welcomes 2022 participants on June 7. Read More »
Machine Learning Fuels Materials Science and Search in Continuous Action Spaces
Using computing resources at NERSC at Berkeley Lab, researchers at Argonne National Laboratory have succeeded in exploring important materials-science questions and demonstrated progress using machine learning to solve difficult search problems. Read More »
Computer Models Show Role of Climate Change in Intense 2020 Hurricane Season
A computer modeling study of the historic 2020 North Atlantic hurricane season found that human-induced climate change played a major role. Read More »
Researchers Narrow Down Mass of Sought-After Axion Particle
Researchers at Berkeley Lab and other institutions have narrowed the range of possible masses for the axion, a theoretical particle that may make up much of the dark matter in the universe. Read More »
New Encoder-Decoder Overcomes Limitations in Scientific Machine Learning
A team of Berkeley Lab computational researchers introduce a new Python-based encoder-decoder framework for complex scientific datasets that overcomes deep learning semantic segmentation issues and widespread adaptability problems. Read More »
New Book Addresses the Unique Artificial Intelligence Needs of High Energy Physics
Berkeley Lab's Paolo Calafiura recently co-edited the book Artificial Intelligence for High Energy Physics, a self-contained, pedagogical introduction to using machine learning models in high energy physics. The book was published in March by the World Scientific Publishing Company, Read More »
San Francisco and Berkeley Lab Team Up on Pioneering Climate Study
Berkeley Lab computational resources are helping the City and County of San Francisco adapt to the Bay Area's changing climate and the extreme storms it is expected to bring. Read More »