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.
Livermore’s machine learning experts aim to provide assurances on performance and enable trust in machine-learning technology through innovative validation and verification techniques.
The Accelerating Therapeutic Opportunities in Medicine (ATOM) consortium is showing “significant” progress in demonstrating that HPC and machine learning tools can speed up the drug discovery process, ATOM co-lead Jim Brase said at a recent webinar.
LLNL participates in the International Parallel and Distributed Processing Symposium (IPDPS) on May 30 through June 3.
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.
Technologies developed through the Next-Generation High Performance Computing Network project are expected to support mission-critical applications for HPC, AI and ML, and high performance data analytics. Applications could include stockpile stewardship, fusion research, advanced manufacturing, climate research and other open science on future ASC HPC systems.
Sponsored by the DSI, LLNL’s winter hackathon took place on February 16–17. In addition to traditional hacking, the hackathon included a special datathon competition in anticipation of the Women in Data Science (WiDS) conference on March 7.
From molecular screening, a software platform, and an online data to the computing systems that power these projects.
LLNL’s cyber programs work across a broad sponsor space to develop technologies addressing sophisticated cyber threats directed at national security and civilian critical infrastructure.