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

GOLLNLP
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.

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

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

Rob Falgout
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,…

Carol Woodward
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…

Mapping cosmic shear to illuminate dark energy
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.

High-performance computing at a crossroads
LLNL's Bruce Hendrickson joins other HPC luminaries in this op-ed about the future of the field.

S&TR cover story: Beginning at the end
The DarkStar inverse design technique blends AI, machine learning, and advanced hydrodynamics simulations to optimize science and engineering solutions starting from the final state.