Topic: Computational Math

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

New research debuting at ICLR 2021 demonstrates a learning-by-compressing approach to deep learning that outperforms traditional methods without sacrificing accuracy.

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.

Three papers address feature importance estimation under distribution shifts, attribute-guided adversarial training, and uncertainty matching in graph neural networks.

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.

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.

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).

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

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.

The hypre library's comprehensive suite of scalable parallel linear solvers makes large-scale scientific simulations possible by solving problems faster.

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

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

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

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.

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

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.

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

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