Topic: Computational Math

Researchers at Brown University, LLNL, and Simula Research Laboratory have developed a new algorithm to help optimizers arrive at solutions in fewer iterations, saving valuable computing time.

LLNL researchers have posters and workshop papers accepted to the 42nd International Conference on Machine Learning on July 13–19.

A new mathematical technique improves the computational efficiency of evaluating the solution in large-scale, high-order meshes on advanced HPC systems.

GLVis is a lightweight tool for accurate and flexible finite element visualization that provides interactive visualizations of general FE meshes and solutions.

This project solves initial value problems for ODE systems, sensitivity analysis capabilities, additive Runge-Kutta methods, DAE systems, and nonlinear algebraic systems.

In a recent study published in the Astrophysical Journal, LLNL researchers developed an innovative approach to map cosmic shear using linear algebra, statistics, and HPC.

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

LLNL's Bruce Hendrickson joins other HPC luminaries in this op-ed about the future of the field.

The DarkStar inverse design technique blends AI, machine learning, and advanced hydrodynamics simulations to optimize science and engineering solutions starting from the final state.

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.

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.

LLNL researchers have achieved a milestone in accelerating and adding features to complex multiphysics simulations run on GPUs, a development that could advance HPC and engineering.

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.