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Mathematical Innovation Advances Complex Simulations for Science’s Toughest Problems

A two-part diagram illustrating a mathematical framework for simplifying complex models. The top diagram shows a V-shaped path of blue arrows enclosing a horizontal line that extends from a point labeled '0' towards an infinity symbol. Small circles along the line represent key numbers describing a system's dynamics. The blue path illustrates a calculation where orange and green segments at the far right can be ignored, allowing scientists to focus on the system's essential features for faster results. The bottom diagram shows a horizontal line with colored tick marks representing a random process visiting different states over time. Above the line, red brackets group larger sets of states into repeating loops, or cycles. Below the line, blue brackets highlight more specific cycles. This illustrates how analyzing these cycles can simplify and speed up complex simulations.

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.

New AI-Powered Hybrid Simulation Reveals How Electrons Drive Chemical Reactions in Liquids

A scientific diagram showing a four-step simulation. Step (a) shows a system of water molecules with an excess electron. Step (b) shows a simplified representation of a single water molecule. Step (c) is a heat map of the system's electrical potential. Step (d) shows the final calculated probability cloud of the electron, derived from the potential in (c).

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

Unlocking the Power of Quantum Computing with Practical Benchmarking Tools

Close-up view of a quantum computer’s superconducting processor housed within a cylindrical, gold-colored cryogenic chamber, featuring multiple stacked plates connected by intricate wiring and metallic rods.

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.

Doudna Storage Partnership Recognized with HPCWire Editors' Choice Award

HPSF Receives HPCwire Readers’ Choice Award for Best Collaboration

Digital simulation generated with the WarpX code depicts blue and yellow shockwaves against a black background.

Unprecedented Perlmutter Simulation Details Quantum Chip

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