Topic: Computational Math

Lawrence Livermore National Lab has named Stefanie Guenther as Computing’s fourth Sidney Fernbach Postdoctoral Fellow in the Computing Sciences. This highly competitive fellowship is named after LLNL’s former Director of Computation and is awarded to exceptional candidates who demonstrate the potential for significant achievements in computational mathematics, computer science, data science, or scientific computing.

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This video describes MFEM (Modular Finite Element Methods), an open-source software library that provides advanced mathematical algorithms for use by scientific applications.

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The Center for Efficient Exascale Discretizations recently released MFEM v4.1, which introduces features important for the nation’s first exascale supercomputers. LLNL's Tzanio Kolev explains.

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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 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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The extreme-scale scientific software development kit (xSDK) is an ecosystem of independently developed math libraries and scientific domain components.

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Computer scientists and applied mathematicians searched for 1 quadrillion “triangles” using 1 million processors on LLNL’s IBM BlueGene/Q Sequoia supercomputer.

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Researchers develop innovative data representations and algorithms to provide faster, more efficient ways to preserve information encoded in data.

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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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The Center for Efficient Exascale Discretizations (CEED) within the ECP involves more than 30 computational scientists from 2 DOE labs (Livermore and Argonne) and 5 universities.

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Simulation workflows for Arbitrary Lagrangian–Eulerian (ALE) methods are highly complex and often require a manual tuning process. There is an urgent need to semi-automate this process to reduce user burden and improve productivity. To address this need, we are developing novel predictive analytics for simulations and an in situ infrastructure for integration of analytics. Our ongoing goals are to predict simulation failures ahead of time and proactively avoid them as much as possible.

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Livermore’s hypre library of solvers makes larger, more detailed simulations possible by solving problems faster than ever before. It offers one of the most comprehensive suites of scalable parallel linear solvers available for large-scale scientific simulation.

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As demonstrated by CASC computer scientists, LLNL's innovative data-driven machine learning techniques teach computers to solve real-world problems.

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LLNL’s Center for Applied Scientific Computing looks back at 2018 papers, presentations, and other activities recognizing research and innovation in data science.

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Profile of LLNL's Lori Diachin, who has over 25 years experience in applied mathematics research including mesh quality improvement, mesh component software, numerical methods, and parallel computing.

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LLNL heads to the SIAM Conference on Computational Science and Engineering (CSE19) in Spokane, Washington, on February 25 to March 1, 2019.

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Highlights include debris an shrapnel modeling at NIF, scalable algorithms for complex engineering systems, magnetic fusion simulation, and data placement optimization on GPUs.

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Highlights include CASC director Jeff Hittinger's vision for the center as well as recent work with PruneJuice DataRaceBench, Caliper, and SUNDIALS.

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LLNL has named Will Pazner as Computation’s third Sidney Fernbach Postdoctoral Fellow in the Computing Sciences.

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In response to a DOE grid optimization challenge, the LLNL-led gollnlp team is developing the mathematical, computational, and software components needed to solve problems of the real-world power grid.

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Highlights include complex simulation codes, uncertainty quantification, discrete event simulation, and the Unify file system.

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