Topic: Computational Science

Highlights include scalable deep learning, high-order finite elements, data race detection, and reduced order models.

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.

StarSapphire is a collection of scientific data mining projects focusing on the analysis of data from scientific simulations, observations, and experiments.

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.

The SAMRAI library is the code base in CASC for exploring application, numerical, parallel computing, and software issues associated with structured adaptive mesh refinement.

Highlights include response to the COVID-19 pandemic, high-order matrix-free algorithms, and managing memory spaces.

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).

Jorge Castro Morales likes having different responsibilities at work. He says, “I’m honored to be working with a diverse team of multidisciplinary experts to resolve very complex problems on a daily basis.”

Computational Scientist Ramesh Pankajakshan came to LLNL in 2016 directly from the University of Tennessee at Chattanooga. But unlike most recent hires from universities, he switched from research professor to professional researcher.

Highlights include perspectives on machine learning and artificial intelligence in science, data driven models, autonomous vehicle operations, and the OpenMP standard 5.0.

Simulation workflows for ALE methods often require a manual tuning process. We are developing novel predictive analytics for simulations and an infrastructure for integration of analytics.

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

AIMS (Analytics and Informatics Management Systems) develops integrated cyberinfrastructure for big climate data discovery, analytics, simulations, and knowledge innovation.

Highlights include the latest work with RAJA, the Exascale Computing Project, algebraic multigrid preconditioners, and OpenMP.

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

When computer scientist Gordon Lau arrived at Lawrence Livermore more than 20 years ago, he was a contractor assigned to a laser isotope separation project.

Highlights include recent LDRD projects, Livermore Tomography Tools, our work with the open-source software community, fault recovery, and CEED.

The NIF Computing team plays a key role in this smoothly running facility, and computer scientist Joshua Senecal supports multiple operational areas.

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.

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.

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

PDES focuses on models that can accurately and effectively simulate California’s large-scale electric grid.

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

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.

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