Topic: Computational Science

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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A high-fidelity, specialized code solves partial differential equations for plasma simulations.

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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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The Enabling Technologies for High-Order Simulations (ETHOS) project performs research of fundamental mathematical technologies for next-generation high-order simulations algorithms.

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High performance computing was key to the December 5 breakthrough at the National Ignition Facility.

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Two supercomputers powered the research of hundreds of scientists at Livermore’s NNSA National Ignition Facility, which recently achieved ignition.

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The major scientific breakthrough decades in the making will pave the way for advancements in national defense and the future of clean power.

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LLNL researchers have developed a novel machine learning (ML) model that can predict 10 distinct polymer properties more accurately than was possible with previous ML models.

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The second annual MFEM workshop brought together the project’s global user and developer community for technical talks, Q&A, and more.

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Researchers will address the challenge of efficiently differentiating large-scale applications for the DOE by building on advances in LLNL’s MFEM finite element library and MIT’s Enzyme AD tool.

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The Earth System Grid Federation, a multi-agency initiative that gathers and distributes data for top-tier projections of the Earth’s climate, is preparing a series of upgrades to make using the data easier and faster while improving how the information is curated.

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

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Presented at the 2022 International Conference on Computational Science, the team’s research introduces metrics that can improve the accuracy of blood flow simulations.

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The second article in a series about the Lab's stockpile stewardship mission highlights computational models, parallel architectures, and data science techniques.

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The first article in a series about the Lab's stockpile stewardship mission highlights the roles of computer simulations and exascale computing.

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The Adaptive Computing Environment and Simulations (ACES) project will advance fissile materials production models and reduce risk of nuclear proliferation.

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The latest generation of Livermore’s workhorse laser physics code promises full integration across research and operations applications.

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LLNL scientists have created a new adjoint waveform tomography model that more accurately simulates earthquake and explosion ground motions. The paper, published in the Journal of Geophysical Research, was selected for an Editor’s Highlight.

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Researchers from LLNL's Energetic Materials Center and Purdue University have leveraged LLNL supercomputing to better understand the chemical reactions that detonate explosives that are “critical to managing the nation’s nuclear stockpile.”

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The latest generation of a laser beam–delay technique owes its success to collaboration, dedication, and innovation.

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An LLNL team will be among the first researchers to perform work on the world’s first exascale supercomputer—Oak Ridge National Laboratory’s Frontier—when they use the system to model cancer-causing protein mutations.

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The Data Science Institute's career panel series continued on June 28 with a discussion of LLNL’s COVID-19 research and development. Four data scientists talked about their work in drug screening, protein–drug compounds, antibody–antigen sequence analysis, and risk factor identification.

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For the first time in the DSC series since the COVID-19 pandemic began in 2020, Lab mentors visited the college campus to provide in-person guidance for five teams of UC Merced students.

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