Topic: Data Science

LLNL's zfp and Variorum software projects are winners. LLNL is a co-developing organization on the winning CANDLE project.

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 Lab’s workhorse visualization tool provides expanded color map features, including for visually impaired users.

This issue highlights some of CASC’s contributions to making controlled laboratory fusion possible at the National Ignition Facility.

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.

Unique among data compressors, zfp is designed to be a compact number format for storing data arrays in-memory in compressed form while still supporting high-speed random access.

The addition of the spatial data flow accelerator into LLNL’s Livermore Computing Center is part of an effort to upgrade the Lab’s cognitive simulation (CogSim) program.

Since 2018, the Lab has seen tremendous growth in its data science community and has invested heavily in related research. Five years later, the Data Science Institute has found its stride.

A novel ML method discovers and predicts key data about networked devices.

Open-source software has played a key role in paving the way for LLNL's ignition breakthrough, and will continue to help push the field forward.

libROM is a library designed to facilitate Proper Orthogonal Decomposition (POD) based Reduced Order Modeling (ROM).

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.