Topic: Computational Math

Held for the first time in a hybrid format, the multi-day MFEM workshop drew participants from around the globe.

LLNL is participating in the 36th annual Supercomputing Conference (SC24) in Atlanta on November 17–22, 2024.

As Computing’s eighth Fernbach Fellow, postdoctoral researcher Robert Stephany will develop specialized algorithms under the mentorship of Youngsoo Choi.

Follow along at your own pace through tutorials of several open-source HPC software projects.

The open-source MFEM library enables application scientists to quickly prototype parallel physics application codes based on PDEs discretized with high-order finite elements.

Developed by LLNL, Colorado, and Purdue researchers, a new approach eases the implementation of curved geometries into computing simulations.

Developed by LLNL and Portland State University researchers, innovative matrix-free solvers offer performance gains for complex multiphysics simulations.

A new method defines a formal specification for convergence, which can be used to derive a set of machine-checkable conditions to guarantee a convergent solution to a differential equation.

The Society for Industrial and Applied Mathematics (SIAM) announced the selection of Lawrence Livermore National Laboratory (LLNL) computational mathematician Ulrike Meier Yang as one of the 2024 Class of SIAM Fellows, the highest honor the organization bestows on its members.

MuyGPs helps complete and forecast the brightness data of objects viewed by Earth-based telescopes.

Can novel mathematical algorithms help scientific simulations leverage hardware designed for machine learning? A team from LLNL’s Center for Applied Scientific Computing aimed to find out.

The Enabling Technologies for High-Order Simulations (ETHOS) project performs research of fundamental mathematical technologies for next-generation high-order simulations algorithms.

The Society for Industrial and Applied Mathematics (SIAM) announced the election of LLNL computational mathematician Carol Woodward as its president-elect and computational scientist Judy Hill as a council member for the organization.

The MFEM virtual workshop highlighted the project’s development roadmap and users’ scientific applications. The event also included Q&A, student lightning talks, and a visualization contest.

LLNL is participating in the 35th annual Supercomputing Conference (SC23), which will be held both virtually and in Denver on November 12–17, 2023.

In recent years, the Lab has boosted its recruiting profile even further by offering the prestigious Sidney Fernbach Postdoctoral Fellowship in the Computing Sciences. The fellowship fosters creative partnerships between new and experienced scientists. In short, it ensures an annual cycle that refreshes advanced research in computer sciences at the Lab.

As Computing’s seventh Fernbach Fellow, postdoctoral researcher Tarik Dzanic will develop new algorithms and test them in computational physics simulations under the mentorship of Bob Anderson.

The Center for Efficient Exascale Discretizations has developed innovative mathematical algorithms for the DOE’s next generation of supercomputers.

Hosted at LLNL, the Center for Efficient Exascale Discretizations’ annual event featured breakout discussions, more than two dozen speakers, and an evening of bocce ball.

CASC computational mathematician Andrew Gillette has always been drawn to mathematics and says it’s about more than just crunching numbers.

This issue highlights some of CASC’s contributions to making controlled laboratory fusion possible at the National Ignition Facility.

With simple mathematical modifications to a common model of clouds and turbulence, LLNL scientists and their collaborators helped minimize nonphysical results.

From wind tunnels and cardiovascular electrodes to the futuristic world of exascale computing, Brian Gunney has been finding solutions for unsolvable problems.

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.