Topic: AI/ML

A multi-institutional consortium aims to speed up the drug discovery pipeline by building predictive, data-driven pharmaceutical models.

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LLNL researchers and colleagues are using machine learning as a virtual magnifying glass to study interesting regions of RAS protein/lipid simulations in higher detail.

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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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LLNL teams conduct research using AI, and the Machine Learning Reading Group serves as a resource for employees to keep one another apprised of developments in this ever-changing field.

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Cindy Gonzales earned a bachelor’s degree, started her master’s degree, and changed careers—all while working at the Lab. Meet one of our newest data scientists.

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LLNL’s Data Science Institute hosted its second annual workshop with the University of California, emphasizing key challenges with machine learning and artificial intelligence in scientific research.

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The partnership will apply DOE-fueled AI capabilities to advance transformative scientific opportunities in biomedical and public health research.

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The NNSA’s first exascale supercomputer, El Capitan, will have a peak performance of more than 1.5 exaflops (1.5 quintillion calculations per second) and an anticipated delivery in late 2022.

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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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Simulation workflows for Arbitrary Lagrangian–Eulerian (ALE) methods are highly complex and often require a manual tuning process. There is an urgent need to semi-automate this process to reduce user burden and improve productivity. To address this need, we are developing novel predictive analytics for simulations and an in situ infrastructure for integration of analytics. Our ongoing goals are to predict simulation failures ahead of time and proactively avoid them as much as possible.

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Brothers and Computation teammates Joe and Sam Eklund discuss their multi-hackathon project using Deep Voice 3.

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Fred Streitz explains LLNL's work to exploit the relationship between simulation and experiments to build predictive codes.

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As demonstrated by CASC computer scientists, LLNL's innovative data-driven machine learning techniques teach computers to solve real-world problems.

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According to DOE secretary Rick Perry, "Accelerating artificial intelligence and machine learning is crucial to strengthening our country’s economic and national security."

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LLNL’s Center for Applied Scientific Computing looks back at 2018 papers, presentations, and other activities recognizing research and innovation in data science.

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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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LLNL employees attended a five-part “Deep Learning 101” course, which introduced the basics of neural networks and machine learning to anyone with a basic knowledge of programming in Python.

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Rushil Anirudh describes the machine learning field as undergoing a “gold rush.”

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Marisa Torres, software developer with LLNL’s Global Security Computing Applications Division, combines her love of biology with coding.

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Highlights include Computation’s annual external review, machine learning for ALE simulations, CFD modeling for low-carbon solutions, seismic modeling, and an in-line floating point compression tool.

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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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LLNL computer scientists use machine learning to model and characterize the performance and ultimately accelerate the development of adaptive applications.

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Researchers are developing enhanced computed tomography image processing methods for explosives identification and other national security applications.

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