Topic: Computational Math

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.

An LLNL Distinguished Member of Technical Staff, Falgout is still finding the fun in problem solving as project leader for two of CASC’s most cutting-edge multigrid method computing projects, hypre and XBraid.

libROM is a library designed to facilitate Proper Orthogonal Decomposition (POD) based Reduced Order Modeling (ROM).

A new component-wise reduced order modeling method enables high-fidelity lattice design optimization.

LLNL’s archives provide a glimpse into the career and contributions of a computing pioneer.

A high-fidelity, specialized code solves partial differential equations for plasma simulations.

An LLNL Distinguished Member of Technical Staff, Carol Woodward consults on a diverse array of projects at the Lab and beyond. “It’s nice because it means I can work at the same place and not just do one thing for a long time,” she says.

Highlights include MFEM community workshops, compiler co-design, HPC standards committees, and AI/ML for national security.

The second annual MFEM workshop brought together the project’s global user and developer community for technical talks, Q&A, and more.

LLNL participates in the International Parallel and Distributed Processing Symposium (IPDPS) on May 30 through June 3.

Winning the best paper award at PacificVis 2022, a research team has developed a resolution-precision-adaptive representation technique that reduces mesh sizes, thereby reducing the memory and storage footprints of large scientific datasets.

Computational mathematician Julian Andrej began using LLNL-developed, open-source software while in Germany. Now at Livermore, he lends his expertise to the Center for Applied Scientific Computing, developing code for next-generation computing hardware.

This project advances research in physics-informed ML, invests in validated and explainable ML, creates an advanced data environment, builds ML expertise across the complex, and more.

The MAPP incorporates multiple software packages into one integrated code so that multiphysics simulation codes can perform at scale on present and future supercomputers.

Highlights include power grid challenges, performance analysis, complex boundary conditions, and a novel multiscale modeling approach.

The MFEM software library provides high-order mathematical algorithms for large-scale scientific simulations. An October workshop brought together MFEM’s global user and developer community for the first time.

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.

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).