![ETHOS logo](/sites/default/files/styles/front_page_card/public/ethos-llnl-project-card.png?itok=xJ1CQwbN)
ETHOS
The Enabling Technologies for High-Order Simulations (ETHOS) project performs research of fundamental mathematical technologies for next-generation high-order simulations algorithms.
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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.
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libROM
libROM is a library designed to facilitate Proper Orthogonal Decomposition (POD) based Reduced Order Modeling (ROM).
![Carolyn Albiston working at a computer (Credit: Department of Energy)](/sites/default/files/styles/front_page_card/public/2023-11/Carolyn%20Albiston%20Final%20Women%20Of%20Energy%20432x285.jpg?itok=NK9AmqPh)
Carolyn Albiston
Carolyn Albiston is a research software engineer in NIF Shot Data Systems. Her career is a culmination of her wide range of varied interests and skills.
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Brian Gunney
From wind tunnels and cardiovascular electrodes to the futuristic world of exascale computing, Brian Gunney has been finding solutions for unsolvable problems.
![Kevin McLoughlin](/sites/default/files/styles/front_page_card/public/2022-05/KevinMcL-leafy.png?itok=o25yU134)
Kevin McLoughlin
Kevin McLoughlin has always been fascinated by the intersection of computing and biology. His LLNL career encompasses award-winning microbial detection technology, a COVID-19 antiviral drug design…
![side-by-side topographical maps of the California Bay Area with Hayward Fault highlighted in blue and earthquake growth depicted in rainbow colors](/sites/default/files/styles/front_page_card/public/2024-06/vol14-leaderboard.png?itok=ExDJvH1x)
CASC Newsletter | Vol 14 | June 2024
This issue highlights some of CASC’s contributions to the DOE's Exascale Computing Project.
![three scientists with different colored abstract backgrounds and the words water, fire, and air at the top](/sites/default/files/styles/front_page_card/public/2024-06/all-scales-video-comp-news2.png?itok=H4mky30N)
Video: The surprising places you’ll find machine learning
LLNL is applying ML to real-world applications on multiple scales. Researchers explain why water filtration, wildfires, and carbon capture are becoming more solvable thanks to groundbreaking data science methodologies on some of the world’s fastest computers.
![concentric circles where the top half is yellow and orange and the bottom half is blue, set on a background of dark green at bottom and light green at top](/sites/default/files/styles/front_page_card/public/2024-06/marbl-solvers-comp-news.png?itok=PDQYmYJU)
Matrix unloaded: GPU-boosted solvers for diffusion physics
Developed by LLNL and Portland State University researchers, innovative matrix-free solvers offer performance gains for complex multiphysics simulations.