Topic: Data-Driven Decisions

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.

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StarSapphire is a collection of scientific data mining projects focusing on the analysis of data from scientific simulations, observations, and experiments.

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An LLNL team developed ML tools that extract and structure information from the text and figures of nanomaterials articles using NLP, image analysis, computer vision, and visualization techniques.

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More than 100 researchers from DOE national labs came to LLNL for the inaugural DOE Data Day workshop to discuss challenges and solutions in accessing, curating, and sharing data.

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ADAPD—Advanced Data Analytics for Proliferation Detection—integrates subject-matter expertise from the DOE’s national laboratories to create new capabilities for analyzing growing global data streams and traditional intelligence data, enabling early warning of nuclear proliferation activities.

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Highlights include perspectives on machine learning and artificial intelligence in science, data driven models, autonomous vehicle operations, and the OpenMP standard 5.0.

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With nearly 100 publications, CASC researcher Jayaraman “Jay” Thiagarajan explores the possibilities of artificial intelligence and machine learning technologies.

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Highlights include the HYPRE library, recent data science efforts, the IDEALS project, and the latest on the Exascale Computing Project.

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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.

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