Topic: AI/ML

As Computing’s ninth Fernbach Fellow, postdoctoral researcher Daniel Nichols will explore how AI can accelerate HPC and computational science under the mentorship of Harshitha Menon.

A new CASC paper proposes unity and clarity around foundation models in computational science, offering an implementation framework inspired by finite element methods.

LLNL researchers have posters and workshop papers accepted to the 42nd International Conference on Machine Learning on July 13–19.

A new cancer drug candidate developed by LLNL, BridgeBio Oncology Therapeutics, and the Frederick National Laboratory for Cancer Research has demonstrated the ability to block tumor growth without triggering a common and debilitating side effect.

Organizers praised Falcone's “extraordinary leadership in driving research directions to advance science and technology at one of America's most vital research institutions.”

As the application of AI across industries accelerates the pace of development, so too must national security remain at the cutting edge, a task requiring extensive collaboration to deploy the nation’s most critical resources.

LLNL and Amazon Web Services are partnering to leverage the power of AI to enhance operations at the National Ignition Facility.

Stanford University professor and AI visionary Fei-Fei Li went beyond technical milestones from her two-decade career into her philosophy of intelligence, ranging from the evolution of vision in the Cambrian Age to modern-day “robot cousins” that can see and assist humans in everyday menial tasks.

A team of researchers from LLNL, Stanford University, and UCLA are using AI and machine learning to find potential treatments for amyotrophic lateral sclerosis and other neurodegenerative diseases in the form of drugs prescribed for other conditions.

LLNL participates in the ISC High Performance Conference (ISC24) on June 10–13.

Wright’s daylong visit included briefings and discussions with LLNL experts in key mission areas including stockpile modernization, fusion energy, AI, cybersecurity and supercomputing.

On the latest episode of The Big Ideas Lab podcast, listeners will get an inside look at this transformation, as LLNL physicists Brian Spears and Kelli Humbird explain how AI is no longer just about chatbots and automating simple tasks.

LLNL researchers have posters and workshop papers accepted to the 13th International Conference on Learning Representations on April 24–28.

Researchers from LLNL, in collaboration with other leading institutions, have successfully used an AI-driven platform to preemptively optimize an antibody to neutralize a broad diversity of SARS-CoV-2 variants.

More than 3,200 LLNL employees participated in the first-ever aiEDGE for Innovation Day on March 26—an event aimed at empowering and equipping the Lab’s workforce to integrate AI into their daily work.

The February 28 event brought together over 1,400 Department of Energy scientists across multiple sites to explore how cutting-edge AI models could transform scientific research.

Increased resource utilization is one goal of new architectures. At Livermore Computing, these include AI accelerators such as Samba Nova and Cerebras systems and El Capitan's Rabbits.

Over the next three years, CASC researchers and collaborators will integrate LLMs into HPC software to boost performance and sustainability.

Highlights include ML techniques for computed tomography, a scalable Gaussian process framework, safe and trustworthy AI, and autonomous multiscale simulations.

The latest issue of LLNL's magazine explains how the world’s most powerful supercomputer helps scientists safeguard the U.S. nuclear stockpile.

LLNL's Bruce Hendrickson joins other HPC luminaries in this op-ed about the future of the field.

The latest episode of the Big Ideas Lab podcast investigates the use of artificial intelligence for drug discovery and other uses.

Todd Gamblin has a well-deserved reputation in the HPC software community as a passionate engineer who enjoys rolling up his sleeves and diving into technical problems. It’s not a stretch to see how he got hooked on HPC.

Presented last fall at a conference, a new approach to software binary analysis incorporates large-scale training data and hierarchical embeddings.

The DarkStar inverse design technique blends AI, machine learning, and advanced hydrodynamics simulations to optimize science and engineering solutions starting from the final state.