Topic: Data Science

AMS is a machine learning solution embedded into scientific applications to automatically replace fine-scale simulations with ancillary models.

LLNL is participating in the 36th annual Supercomputing Conference (SC24) in Atlanta on November 17–22, 2024.

Learn about the game-changing potential of El Capitan and discover how it will not only transform HPC and AI but also revolutionize scientific research across multiple domains.

A groundbreaking multidisciplinary team is combining the power of exascale computing with AI, advanced workflows, and GPU acceleration to advance scientific innovation and revolutionize digital design.

A CASC researcher and collaborators study model failure and resilience in a paper accepted to the 2024 International Conference on Machine Learning.

LLNL researchers study model robustness in a paper accepted to the 2024 International Conference on Machine Learning.

The collaboration has enabled expanding systems of the same architecture as LLNL’s upcoming exascale supercomputer, El Capitan, featuring AMD’s cutting-edge MI300A processors.

In two papers from the 2024 International Conference on Machine Learning, Livermore researchers investigate how LLMs perform under measurable scrutiny.

To keep employees abreast of the latest tools, two data science–focused projects are under way as part of Lawrence Livermore’s Institutional Scientific Capability Portfolio.

The proposed Frontiers in Artificial Intelligence for Science, Security and Technology (FASST) initiative will advance national security; attract and build a talented workforce; harness AI for scientific discovery; address energy challenges; develop technical expertise necessary for AI governance.

This issue highlights some of CASC’s contributions to the DOE's Exascale Computing Project.

LLNL is applying ML to real-world applications on multiple scales. Researchers explain why water filtration, wildfires, and carbon capture are becoming more solvable thanks to groundbreaking data science methodologies on some of the world’s fastest computers.

In a milestone for supercomputing-aided drug design, LLNL and BridgeBio Oncology Therapeutics today announced clinical trials have begun for a first-in-class medication that targets specific genetic mutations implicated in many types of cancer.

Two LLNL teams have come up with ingenious solutions to a few of the more vexing difficulties. For their efforts, they’ve won awards coveted by scientists in the technology fields.

Held May 7–8 in Washington, DC, the Special Competitive Studies Project (SCSP) AI Expo showcased groundbreaking initiatives in AI and emerging technologies. Kim Budil and other Lab speakers presented at center stage and the DOE exhibition booth.

LLNL’s HPC capabilities play a significant role in international science research and innovation, and Lab researchers have won 10 R&D 100 Awards in the Software–Services category in the past decade.

zfp is an open-source C/C++ library for compressed floating-point and integer arrays that support high throughput read and write random access.

In a groundbreaking development for addressing future viral pandemics, a multi-institutional team involving LLNL researchers has successfully combined an AI-backed platform with supercomputing to redesign and restore the effectiveness of antibodies whose ability to fight viruses has been compromi

Throughout the workshop, speakers, panelists and attendees focused on algorithm development, the potential dangers of superhuman AI systems and the importance of understanding and mitigating the risks to humans, as well as urgent measures needed to address the risks both scientifically and politically.

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.