Topic: AI/ML

With this year’s results, the Lab has now collected a total of 179 R&D 100 awards since 1978. The awards will be showcased at the 61st R&D 100 black-tie awards gala on Nov. 16 in San Diego.

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.

Led by Argonne National Lab and including an LLNL collaborator, a research team aims to provide the security necessary to study life-threatening medical issues without violating patient privacy.

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

The event brought together 35 University of California students—ranging from undergraduates to graduate-level students from a diversity of majors—to work in groups to solve four key tasks, using actual electrocardiogram data to predict heart health.

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 “crystal ball” that provided increased pre-shot confidence in LLNL's fusion ignition breakthrough involved a combination of detailed HPC design and a suite of methods combining physics-based simulation with machine learning—called cognitive simulation, or CogSim.

The report lays out a comprehensive vision for the DOE Office of Science and NNSA to expand their work in scientific use of AI by building on existing strengths in world-leading high performance computing systems and data infrastructure.

The new model addresses a problem in simulating RAS behavior, where conventional methods come up short of reaching the time- and length-scales needed to observe biological processes of RAS-related cancers.

A principal investigator at LLNL shares how machine learning on the world’s fastest systems catalyzed the lab’s breakthrough.

Collaborative autonomy software apps allow networked devices to detect, gather, identify and interpret data; defend against cyber-attacks; and continue to operate despite infiltration.

From our fall 2022 hackathon, watch as participants trained an autonomous race car with reinforcement learning algorithms.

A new collaboration will leverage advanced LLNL-developed software to create a “digital twin” of the near-net shape mill-products system for producing aerospace parts.

Adding machine learning and other artificial intelligence methods to the feedback cycle of experimentation and computer modeling can accelerate scientific discovery.

High performance computing was key to the December 5 breakthrough at the National Ignition Facility.

Two supercomputers powered the research of hundreds of scientists at Livermore’s NNSA National Ignition Facility, which recently achieved ignition.

LLNL researchers have developed a novel machine learning (ML) model that can predict 10 distinct polymer properties more accurately than was possible with previous ML models.

The 2022 International Conference for High Performance Computing, Networking, Storage, and Analysis (SC22) returned to Dallas as a large contingent of LLNL staff participated in sessions, panels, paper presentations and workshops centered around HPC.

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

The award recognizes progress in the team's ML-based approach to modeling ICF experiments, which has led to the creation of faster and more accurate models of ICF implosions.

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

The second article in a series about the Lab's stockpile stewardship mission highlights computational models, parallel architectures, and data science techniques.

The Adaptive Computing Environment and Simulations (ACES) project will advance fissile materials production models and reduce risk of nuclear proliferation.

More than 100 million smart meters have been installed in the U.S. to record and communicate electric consumption, voltage, and current to consumers and grid operators. LLNL has developed GridDS to help make the most of this data.

An LLNL team will be among the first researchers to perform work on the world’s first exascale supercomputer—Oak Ridge National Laboratory’s Frontier—when they use the system to model cancer-causing protein mutations.