Topic: Computational Science

LLNL's Jay Thiagarajan joins the Data Skeptic podcast to discuss his recent paper "Calibrating Healthcare AI: Towards Reliable and Interpretable Deep Predictive Models." The episode runs 35:50.

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Combining computer simulations with ultra-high-speed X-ray imaging, LLNL researchers have discovered a way to reduce defects in parts built through a laser-based metal 3D-printing process.

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In this video from the Stanford HPC Conference, Katie Lewis presents "The Incorporation of Machine Learning into Scientific Simulations at Lawrence Livermore National Laboratory."

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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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LLNL researchers have identified an initial set of therapeutic antibody sequences, designed in a few weeks using machine learning and supercomputing, aimed at binding and neutralizing SARS-CoV-2.

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Alyson Fox is a math geek. She has three degrees in the subject—including a Ph.D. in Applied Mathematics from the University of Colorado at Boulder—and her passion for solving complex challenges drives her work at LLNL’s Center for Applied Scientific Computing (CASC).

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The early-March event was the third annual WiDS Livermore event, featuring speakers, a career panel, mentoring, and a livestream.

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LLNL has infrastructure, unique research capabilities, and a dedicated team of scientists and engineers supporting the fight against COVID-19.

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LLNL scientists are contributing to the global fight against COVID-19 by combining AI/ML, bioinformatics, and supercomputing to help discover candidates for new antibodies and pharmaceutical drugs.

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The White House announced the COVID-19 HPC Consortium to provide access to the world’s most powerful HPC resources that can advance the pace of scientific discovery in the fight to stop the virus.

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LLNL bested more than two dozen teams to place first overall in Challenge 1 of the DOE Grid Optimization Competition, aimed at developing a more reliable, resilient, and secure U.S. electrical grid.

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On January 31, 2020, the Sequoia supercomputer and its file system were decommissioned after nearly 8 years of remarkable service and achievements.

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Laser-fusion researchers have turned to machine-learning techniques to seek the combinations of laser pulse characteristics and target design needed to optimize target implosions for ICF.

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

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Jorge Castro Morales likes having different responsibilities at work. He says, “I’m honored to be working with a diverse team of multidisciplinary experts to resolve very complex problems on a daily basis.”

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The paper describes the workflow driving a first-of-its-kind multiscale simulation on predictively modeling the dynamics of RAS proteins and interactions with lipids.

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Twelve projects are awarded funding for the High Performance Computing for Energy Innovation Program, which leverages DOE’s HPC facilities to improve energy efficiency and manufacturing processes.

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The HPC4EI Initiative seeks industry partners to work with DOE labs to solve key technical challenges in manufacturing and mobility.

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At the National Ignition Facility, simulations help assess the risk of damage from target debris and shrapnel dispersal during high-energy laser shots.

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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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After years of preparation, LLNL’s upgraded Ares code runs a 98-billion-element simulation on 16,384 GPUs on the Sierra supercomputer.

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Computational Scientist Ramesh Pankajakshan came to LLNL in 2016 directly from the University of Tennessee at Chattanooga. But unlike most recent hires from universities, he switched from research professor to professional researcher.

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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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As part of the Department of Energy’s role in the fight against cancer, scientists are building tools that use supercomputers to solve problems in entirely new ways.

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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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