Topic: Data Science

LLNL’s fusion ignition breakthrough, more than 60 years in the making, was enabled by a combination of traditional fusion target design methods, HPC, and AI techniques.

With over 90 people in attendance, including those attending online and in person, the WiDS Livermore conference was once again successful in facilitating the exchange of information and fresh ideas.

By taking weather variables such as wildfire, flooding, wind, and sunlight that directly impact the electrical grid into consideration, researchers can improve electrical grid model projections for a more stable future.

MuyGPs helps complete and forecast the brightness data of objects viewed by Earth-based telescopes.

The Lab is hosting two related WiDS events: First is a datathon on February 28, then the annual regional conference on March 13. These hybrid events are free and open to everyone.

New research reveals subtleties in the performance of neural image compression methods, offering insights toward improving these models for real-world applications.

LLNL is participating in the 35th annual Supercomputing Conference (SC23), which will be held both virtually and in Denver on November 12–17, 2023.

Merlin is an open-source workflow orchestration and coordination tool that makes it easy to build, run, and process large-scale workflows.

Cindy Gonzales earned a bachelor’s degree and master’s degree and changed careers—all while working at the Lab. Meet the deputy director of LLNL’s Data Science Institute.

CASC computational mathematician Andrew Gillette has always been drawn to mathematics and says it’s about more than just crunching numbers.

Using explainable artificial intelligence techniques can help increase the reach of machine learning applications in materials science, making the process of designing new materials much more efficient.

The Lab’s workhorse visualization tool provides expanded color map features, including for visually impaired users.

This issue highlights some of CASC’s contributions to making controlled laboratory fusion possible at the National Ignition Facility.

A novel ML method discovers and predicts key data about networked devices.

libROM is a library designed to facilitate Proper Orthogonal Decomposition (POD) based Reduced Order Modeling (ROM).

A new component-wise reduced order modeling method enables high-fidelity lattice design optimization.

Highlights include MFEM community workshops, compiler co-design, HPC standards committees, and AI/ML for national security.

High-precision numerical data from computer simulations, observations, and experiments is often represented in floating point and can easily reach terabytes to petabytes of storage.

LLNL is participating in the 34th annual Supercomputing Conference (SC22), which will be held both virtually and in Dallas on November 13–18, 2022.

In a time-trial competition, participants trained an autonomous race car with reinforcement learning algorithms.

After 10 years and 33 hackathons, nothing can stop this beloved tradition.

The Earth System Grid Federation is a web-based tool set that powers most global Earth system research.

Angeline Lee simultaneously serves as a group leader, contributes to programmatic projects, and studies for her bachelor’s degree.

Winning the best paper award at PacificVis 2022, a research team has developed a resolution-precision-adaptive representation technique that reduces mesh sizes, thereby reducing the memory and storage footprints of large scientific datasets.

LLNL participates in the International Parallel and Distributed Processing Symposium (IPDPS) on May 30 through June 3.