Topic: Data Science

The prestigious fellow designation is a lifetime honorific title and honors SIAM members who have made outstanding contributions to fields served by the organization.

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

Women data scientists, Lab employees, and other attendees interested in the field gathered at the Livermore Valley Open Campus for the annual Livermore Women in Data Science (WiDS) regional event held in conjunction with the global WiDS conference.

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

Register by February 27 for this free, hybrid Women in Data Science event. Everyone is welcome.

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.

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.

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.

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

Two LLNL-led teams received SciVis Test of Time awards at the 2022 IEEE VIS conference for papers that have achieved lasting relevancy in the field of scientific visualization.

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

Researchers are starting a three-year project aimed at improving methods for visual analysis of large heterogeneous datasets as part of a recent DOE funding opportunity.

The Earth System Grid Federation, a multi-agency initiative that gathers and distributes data for top-tier projections of the Earth’s climate, is preparing a series of upgrades to make using the data easier and faster while improving how the information is curated.

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

The new oneAPI Center of Excellence will involve the Center for Applied Scientific Computing and accelerate ZFP compression software to advance exascale computing.

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