Topic: Computational Science

As part of the Exascale Computing Project’s ExaSGD project, a team including LLNL researchers ran HiOp, an open source optimization solver, on 9,000 nodes of Oak Ridge National Laboratory’s Frontier exascale supercomputer.

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.

With simple mathematical modifications to a common model of clouds and turbulence, LLNL scientists and their collaborators helped minimize nonphysical results.

From wind tunnels and cardiovascular electrodes to the futuristic world of exascale computing, Brian Gunney has been finding solutions for unsolvable problems.

Responding to a DOE grid optimization challenge, an LLNL-led team developed the mathematical, computational, and software components needed to solve problems of the real-world power grid.

Computer scientist Vanessa Sochat talks to BSSw about a recent effort to survey software developer needs at LLNL.

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.

For the physicists, computer scientists, and code developers who have worked on fusion for decades, computer simulations have been inexorably tied to the National Ignition Facility’s quest for ignition.

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.

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.

A high-fidelity, specialized code solves partial differential equations for plasma simulations.

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.

Combining specialized software tools with heterogeneous HPC hardware requires an intelligent workflow performance optimization strategy.

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

The second annual MFEM workshop brought together the project’s global user and developer community for technical talks, Q&A, and more.

Presented at the 2022 International Conference on Computational Science, the team’s research introduces metrics that can improve the accuracy of blood flow simulations.

The Earth System Grid Federation is a web-based tool set that powers most global Earth system research.

The latest generation of a laser beam–delay technique owes its success to collaboration, dedication, and innovation.

Kevin McLoughlin has always been fascinated by the intersection of computing and biology. His LLNL career encompasses award-winning microbial detection technology, a COVID-19 antiviral drug design pipeline, and work with the ATOM consortium.

As group leader and application developer in the Global Security Computing Applications Division, Jarom Nelson develops intrusion detection and access control software.