Topic: Scientific Visualization

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

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

The Data and Visualization efforts in the DOE’s Exascale Computing Project provide an ecosystem of capabilities for data management, analysis, lossy compression, and visualization.

Using explainable artificial intelligence techniques can help increase the reach of machine learning applications in materials science, making the process of designing new materials much more efficient.

The Lab’s workhorse visualization tool provides expanded color map features, including for visually impaired users.

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

Highlights include perspectives on machine learning and artificial intelligence in science, data driven models, autonomous vehicle operations, and the OpenMP standard 5.0.

SOAR (Stateless, One-pass Adaptive Refinement) is a view-dependent mesh refinement and rendering algorithm.

This project's techniques reduce bandwidth requirements for large unstructured data by making use of data compression and optimizing the layout of the data for better locality and cache reuse.

LLNL and University of Utah researchers have developed an advanced, intuitive method for analyzing and visualizing complex data sets.