Topic: Computational Math

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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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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The Exascale Computing Project (ECP) 2022 Community Birds-of-a-Feather Days will take place May 10–12 via Zoom. The event provides an opportunity for the HPC community to engage with ECP teams to discuss our latest development efforts.

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LLNL's DMTS awards program offers advancement for scientific leaders who choose the research track over the management ladder. Read more about computational mathematician Rob Falgout.

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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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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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Computational mathematician Julian Andrej began using LLNL-developed, open-source software while in Germany. Now at Livermore, he lends his expertise to the Center for Applied Scientific Computing, developing code for next-generation computing hardware.

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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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The MAPP incorporates multiple software packages into one integrated code so that multiphysics simulation codes can perform at scale on present and future supercomputers.

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libROM is a library designed to facilitate Proper Orthogonal Decomposition (POD) based Reduced Order Modeling (ROM).

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The MFEM software library provides high-order mathematical algorithms for large-scale scientific simulations. An October workshop brought together MFEM’s global user and developer community for the first time.

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An LLNL mathematician and collaborators have developed a machine learning–based technique capable of deriving a mathematical model for the motion of binary black holes from gravitational wave data.

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In a project with U.S. Steel, LLNL computational physicists built models of the hot-rolling process to run on LLNL’s HPC platforms.

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As Computing’s fifth Fernbach Fellow, postdoctoral researcher Steven Roberts will develop, analyze, and implement new time integration methods.

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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.

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The 2021 Conference on Computer Vision and Pattern Recognition features two papers co-authored by an LLNL researcher targeted at understanding robust machine learning models.

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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 hypre team's latest work gives scientists the ability to efficiently utilize modern GPU-based extreme scale parallel supercomputers to address many scientific problems.

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SIAM announced its 2021 Class of Fellows, including LLNL computational mathematician Rob Falgout. Falgout is best known for his development of multigrid methods and for hypre, one of the world’s most popular parallel multigrid codes.

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Our researchers will be well represented at the virtual SIAM Conference on Computational Science and Engineering (CSE21) on March 1–5. SIAM is the Society for Industrial and Applied Mathematics with an international community of more than 14,500 individual members.

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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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