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Thermodynamic Computing Advances with Design and Training

Harnessing AI for Particle Accelerator Innovation

2026 CSA Symposium Helps Researchers Amplify Their Scientific Impact

A group of five diverse male speakers stand in a line at a symposium, facing towards the left of the frame. They are dressed in business casual attire, including button-down shirts and slacks. A large monitor in the background displays an image of the group standing in the same formation.

AMCR’s Aditi Krishnapriyan Receives 2025 Early Career Award

Aditi Krishnapriyan, a woman with long dark hair, smiles while standing outside in front of green trees and a bright sky.

SciData’s Drew Paine selected as BSSw Fellow

Expert Insights: Drew Paine

NERSC Quantum Report Projects Progress for Science

Photo of the Aquila quantum computer at QuEra Computing

Berkeley Lab Researchers Evaluate Generative AI Models for Filling Scientific Imaging Gaps

A comparative infographic showcasing the results of different generative AI models in creating scientific images. The infographic is divided into three vertical sections, labeled 'Ceramics,' 'Plants,' and 'Rocks.' Each section contains a grid of images comparing a 'Raw' scientific source image to AI-generated versions created by models including DCGAN, StyleGAN, DALL-E2, and DALL-E3. The 'Ceramics' section shows microscopic views of packed particles, the 'Plants' section displays images of seedlings and root systems, and the 'Rocks' section features grayscale micro-scans of rock sediment, illustrating the varying success of different AI models in reproducing scientifically accurate images.

Happy Holidays from Berkeley Lab’s Computing Sciences Area

Decorative image with holly berries and leaves and the text "Happy Holidays from Berkeley Lab's Computing Sciences Area"

NERSC Network Upgrades Pave the Way for a Faster Future

NERSC Co-Hosts HPC Bootcamp to Train Next-Gen Computational Scientists

New AI Model Advances Long-Term, Multi-Scale Prediction of Complex Physical Systems

Four-panel visualizations for three different physical systems are arranged in three rows, labeled (a), (b), and (c). Each row corresponds to a specific system: (a) plasma turbulence (MHD), (b) fluid flow (Navier-Stokes), and (c) shallow-water dynamics. Within each row, the panels display (from left to right): the ground truth state, the StFT-F model prediction, the spatial distribution of prediction error (residual), and the predicted uncertainty. Colorbars beside each panel indicate the value ranges, with red and blue representing positive and negative values. The visualizations show that areas with higher errors generally coincide with regions of higher predicted uncertainty.
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