Topic: Computational Math

Six LLNL Computing researchers have been named Distinguished Members of Technical Staff in recognition of their extraordinary scientific and technical contributions.

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The hypre library's comprehensive suite of scalable parallel linear solvers makes large-scale scientific simulations possible by solving problems faster.

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The latest chapter in the nearly 30-year history of hypre includes a new semi-structured algebraic multigrid solver and support for mixed numerical precision at runtime.

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This project constructs coarse time grids and uses each solution to improve the next finer-scale solution, simultaneously updating a solution guess over the entire space-time domain.

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Our researchers will be well represented at the SIAM Conference on Parallel Processing for Scientific Computing (PP26) on March 3–6. SIAM is the Society for Industrial and Applied Mathematics with an international community of more than 14,000 individual members.

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GLVis is a lightweight tool for accurate and flexible finite element visualization that provides interactive visualizations of general FE meshes and solutions.

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This project solves initial value problems for ODE systems, sensitivity analysis capabilities, additive Runge-Kutta methods, DAE systems, and nonlinear algebraic systems.

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A sophisticated, cost-effective framework combines HPC, ML models, and mathematical algorithms to optimize power grid stability and security.

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LLNL’s presence, which included dozens of sessions, including tutorials, workshops, paper presentations and birds-of-a-feather meetings was felt across virtually every major event of the week.

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Widely viewed as the highest recognition in HPC, the Gordon Bell Prize recognizes innovations that push the limits of computational performance, scalability and scientific impact on pressing real-world problems.

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The open-source MFEM library enables application scientists to quickly prototype parallel physics application codes based on PDEs discretized with high-order finite elements.

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Five years strong, the MFEM workshop fosters connection and collaboration among the computational math community.

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Morphing an interest in simulation into a career in sophisticated software development, Yohann Dudouit helps the Laboratory visualize complex scientific phenomena in the national interest.

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Scientists at LLNL have helped develop an advanced, real-time tsunami forecasting system—powered by El Capitan, the world’s fastest supercomputer—that could dramatically improve early warning capabilities for coastal communities near earthquake zones.

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A new CASC paper proposes unity and clarity around foundation models in computational science, offering an implementation framework inspired by finite element methods.

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Researchers at Brown University, LLNL, and Simula Research Laboratory have developed a new algorithm to help optimizers arrive at solutions in fewer iterations, saving valuable computing time.

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LLNL researchers have posters and workshop papers accepted to the 42nd International Conference on Machine Learning on July 13–19.

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A new mathematical technique improves the computational efficiency of evaluating the solution in large-scale, high-order meshes on advanced HPC systems.

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In a recent study published in the Astrophysical Journal, LLNL researchers developed an innovative approach to map cosmic shear using linear algebra, statistics, and HPC.

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LLNL's Bruce Hendrickson joins other HPC luminaries in this op-ed about the future of the field.

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The DarkStar inverse design technique blends AI, machine learning, and advanced hydrodynamics simulations to optimize science and engineering solutions starting from the final state.

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