Topic: Computational Math

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 the third Sidney Fernbach Postdoctoral Fellow in the Computing Sciences.

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Responding to a DOE grid optimization challenge, an LLNL-led team developed 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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Highlights include the directorate's annual external review, machine learning for ALE simulations, CFD modeling for low-carbon solutions, seismic modeling, and an in-line floating point compression tool.

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This first-principles simulation method models the interaction of laser light with diffraction gratings, giving scientists a powerful tool to predict the performance of a laser compressor.

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Highlights include the HYPRE library, recent data science efforts, the IDEALS project, and the latest on the Exascale Computing Project.

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The Extreme Resilient Discretization project (ExReDi) was established to address these challenges for algorithms common for fluid and plasma simulations.

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Newly developed mathematical techniques reveal important tools for data mining analysis.

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Based on a discretization and time-stepping algorithm, these equations include a local order parameter, a quaternion representation of local orientation, and species composition.

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This scalable first-principles MD algorithm with O(N) complexity and controllable accuracy is capable of simulating systems that were previously impossible with such accuracy.

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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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High-resolution finite volume methods are being developed for solving problems in complex phase space geometries, motivated by kinetic models of fusion plasmas.

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Researchers are testing and enhancing a neutral particle transport code and its algorithm to ensure that they successfully scale to larger and more complex computing systems.

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LLNL and University of Utah researchers have developed an advanced, intuitive method for analyzing and visualizing complex data sets.

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These Fortran solvers tackle the initial value problem for ODE systems. The collection includes solvers for systems given in both explicit and linearly implicit forms.

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The NSDE project is focused on research and development of nonlinear solvers and sensitivity analysis techniques for nonlinear, time-dependent, and steady-state partial differential equations.

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The flourishing of simulation-based scientific discovery has also resulted in the emergence of the UQ discipline, which is essential for validating and verifying computer models.

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These methods for solving hyperbolic wave propagation problems allow for complex geometries, realistic boundary and interface conditions, and arbitrary heterogeneous material properties.

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A new algorithm for use with first-principles molecular dynamics codes enables the number of atoms simulated to be proportional to the number of processors available.

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BLAST is a high-order finite element hydrodynamics research code that improves the accuracy of simulations and provides a path to extreme parallel computing and exascale architectures.

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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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