Topic: Data Science

A new component-wise reduced order modeling method enables high-fidelity lattice design optimization.

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Women data scientists, Lab employees, and other attendees interested in the field gathered at the Livermore Valley Open Campus for the annual Livermore Women in Data Science (WiDS) regional event held in conjunction with the global WiDS conference.

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A principal investigator at LLNL shares how machine learning on the world’s fastest systems catalyzed the lab’s breakthrough.

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Register by February 27 for this free, hybrid Women in Data Science event. Everyone is welcome.

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Collaborative autonomy software apps allow networked devices to detect, gather, identify and interpret data; defend against cyber-attacks; and continue to operate despite infiltration.

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From our fall 2022 hackathon, watch as participants trained an autonomous race car with reinforcement learning algorithms.

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A new collaboration will leverage advanced LLNL-developed software to create a “digital twin” of the near-net shape mill-products system for producing aerospace parts.

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Adding machine learning and other artificial intelligence methods to the feedback cycle of experimentation and computer modeling can accelerate scientific discovery.

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High-precision numerical data from computer simulations, observations, and experiments is often represented in floating point and can easily reach terabytes to petabytes of storage.

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In a time-trial competition, participants trained an autonomous race car with reinforcement learning algorithms.

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After 10 years and 33 hackathons, nothing can stop this beloved tradition.

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LLNL participates in the CMD-IT/ACM Richard Tapia Celebration of Diversity in Computing Conference (Tapia2022) on September 7–10.

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The Earth System Grid Federation is a web-based tool set that powers most global Earth system research.

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Angeline Lee simultaneously serves as a group leader, contributes to programmatic projects, and studies for her bachelor’s degree.

People Highlight

Winning the best paper award at PacificVis 2022, a research team has developed a resolution-precision-adaptive representation technique that reduces mesh sizes, thereby reducing the memory and storage footprints of large scientific datasets.

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LLNL participates in the International Parallel and Distributed Processing Symposium (IPDPS) on May 30 through June 3.

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Kevin McLoughlin has always been fascinated by the intersection of computing and biology. His LLNL career encompasses award-winning microbial detection technology, a COVID-19 antiviral drug design pipeline, and work with the ATOM consortium.

People Highlight

From molecular screening, a software platform, and an online data to the computing systems that power these projects.

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LLNL’s cyber programs work across a broad sponsor space to develop technologies addressing sophisticated cyber threats directed at national security and civilian critical infrastructure.

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This project advances research in physics-informed ML, invests in validated and explainable ML, creates an advanced data environment, builds ML expertise across the complex, and more.

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LLNL is participating in the 33rd annual Supercomputing Conference (SC21), which will be held both virtually and in St. Louis on November 14–19, 2021.

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