LivIT tackles challenges of workforce safety, telecommuting, cyber security protocols, National Ignition Facility software updates, and more.
Topic: Data-Driven Decisions
The ADAPD program held a virtual meeting to highlight science-based, data-driven analysis work to advance AI innovation and AI-enabled systems to enhance the U.S. nuclear proliferation detection activities.
Highlights include scalable deep learning, high-order finite elements, data race detection, and reduced order models.
Led by computational scientist Youngsoo Choi, the Data-Driven Physical Simulation reading group has been meeting biweekly since October 2019. The pandemic almost disbanded the group... until it turned into a virtual seminar series.
StarSapphire is a collection of scientific data mining projects focusing on the analysis of data from scientific simulations, observations, and experiments.
ADAPD integrates expertise from DOE national labs to analyze growing global data streams and traditional intelligence data, enabling early warning of nuclear proliferation activities.
Highlights include perspectives on machine learning and artificial intelligence in science, data driven models, autonomous vehicle operations, and the OpenMP standard 5.0.
With nearly 100 publications, CASC researcher Jayaraman “Jay” Thiagarajan explores the possibilities of artificial intelligence and machine learning technologies.
Highlights include the HYPRE library, recent data science efforts, the IDEALS project, and the latest on the Exascale Computing Project.
Jeene Villanueva develops enterprise modeling tools that help DOE decision makers gain insight into the challenging problems faced by the U.S. nuclear weapons complex.