Highlights include power grid challenges, performance analysis, complex boundary conditions, and a novel multiscale modeling approach.
Topic: Transport
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Highlights include scalable deep learning, high-order finite elements, data race detection, and reduced order models.
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AI/ML | Biology/Biomedicine | Computational Math | Computational Science | Data Science | Data-Driven Decisions | Debugging and Correctness | Discrete Mathematics | HPC Systems and Software | Hydrodynamics | Open-Source Software | Performance, Portability, and Productivity | Power Management | Programming Languages and Models | Solvers | Transport
Proxy apps serve as specific targets for testing and simulation without the time, effort, and expertise that porting or changing most production codes would require.
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Co-Design | Computational Math | Computational Science | Discrete Mathematics | Emerging Architectures | HPC Systems and Software | Hydrodynamics | Open-Source Software | Performance, Portability, and Productivity | Programming Languages and Models | Proxy Apps | Software Engineering | Software Libraries | Solvers | Storage, File Systems, and I/O | Transport
The SAMRAI library is the code base in CASC for exploring application, numerical, parallel computing, and software issues associated with structured adaptive mesh refinement.
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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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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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LLNL researchers are testing and enhancing a neutral particle transport code and the algorithm on which the code relies to ensure that they successfully scale to larger and more complex computing systems.
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