Topic: Scientific ML

LLNL held its first-ever Machine Learning for Industry Forum (ML4I) on August 10–12. Co-hosted by the Lab’s High-Performance Computing Innovation Center and Data Science Institute, the virtual event brought together more than 500 attendees from the Department of Energy (DOE) complex, commercial companies, professional societies, and academia.

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New research debuting at ICLR 2021 demonstrates a learning-by-compressing approach to deep learning that outperforms traditional methods without sacrificing accuracy.

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The Accelerating Therapeutics for Opportunities in Medicine consortium, of which LLNL is part, announced the U.S. Department of Energy’s Argonne, Brookhaven and Oak Ridge national labs are joining the consortium to further develop ATOM’s AI-driven drug discovery platform.

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Three papers address feature importance estimation under distribution shifts, attribute-guided adversarial training, and uncertainty matching in graph neural networks.

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An LLNL team has developed a “Learn-by-Calibrating” method for creating powerful scientific emulators that could be used as proxies for far more computationally intensive simulators.

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Livermore Computing's CTO Bronis de Supinski discusses the Lab's early work with, and vision for, the Cerebras-Lassen hardware integration.

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LLNL and Cerebras Systems have installed the company’s AI computer into Lassen, making LLNL the first institution to integrate the cutting-edge AI platform with a supercomputer.

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Lawrence Livermore National Lab has named Stefanie Guenther as Computing’s fourth Sidney Fernbach Postdoctoral Fellow in the Computing Sciences. This highly competitive fellowship is named after LLNL’s former Director of Computation and is awarded to exceptional candidates who demonstrate the potential for significant achievements in computational mathematics, computer science, data science, or scientific computing.

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Surrogate models supported by neural networks could lead to new insights in complicated physics problems such as inertial confinement fusion.

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