Machine Learning
LLNL computer scientists use machine learning to model and characterize the performance and ultimately accelerate the development of adaptive applications.
Topological Analysis
LLNL and University of Utah researchers have developed an advanced, intuitive method for analyzing and visualizing complex data sets.
HZIP
hzip 1.0.1 is a C++ library for lossless compression of structured and unstructured meshes composed of cells with hypercube topology.
For better CT images, new deep learning tool helps fill in the blanks
LLNL researchers collaborated with Washington University in St. Louis to devise a state-of-the-art ML–based reconstruction tool for when high-quality computed tomography data is in low supply.
Data Days brings DOE labs together for discussions on data management and more
Data researchers, developers, data managers, and program managers from national laboratories visited LLNL to discuss the latest in data management, sharing, and accessibility at the 2023 DOE Data Days (D3) workshop.
SC23 event calendar
LLNL is participating in the 35th annual Supercomputing Conference (SC23), which will be held both virtually and in Denver on November 12–17, 2023.