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We develop innovative tools and techniques for scientific visualization and high performance computing to advance measurement science.
The group develops novel algorithms and implementations for parallel and distributed computation in a variety of hardware environments. Researchers create innovative techniques for immersive scientific visualization in a variety of local and distributed hardware environments. They also develop and utilize techniques and tools of mathematical analysis, parallel and distributed computing, and visualization to improve measurement science, including the extension of traditional measurement science to the virtual world. Lastly, the group contributes to the development of scientific applications at NIST through multidisciplinary collaborative projects.
Visualization of a mathematical model of the flow of concrete with interactive controls
A high-end race car engine needs all its components tuned and working together precisely to deliver top-quality performance. The same can be said about the
Electrons confined in arrays of semiconductor nanostructures, called quantum dots (QDs), are a promising quantum computing approach. Due to the ease of control of the relevant parameters, fast measurement of the spin and charge states, relatively long decoherence times, and their potential for
Current HPC technology broadly consists of:
Leadership systems, such as the Department of Energy INCITE systems.
Production Systems, such as clusters
We work on both types of systems. For example, our work on the Rheology or Flow Properties of Concrete (see pages 15-16 in the latest
Current visualization technology provides a full range of hardware and techniques from static two-dimensional plots, to interactive three-dimensional images projected onto a monitor, to large screen fully immersive systems allowing the user to interact on a human scale.
Immersive virtual reality
Physics graduate studies are substantial efforts on the part of individual students, departments, and institutions of higher education. Understanding the
Barry I. Schneider, Anthony J. Kearsley, Walid Keyrouz, Thomas C. Allison, chen qu
Graph neural networks have been successfully applied to machine learning models related to molecules and crystals, due to the similarity between a molecule
Daniel Schug, Tyler Kovach, Jared Benson, Mark Eriksson, Justyna Zwolak
In the physical sciences, there is an increased need for robust feature representations of image data: image acquisition, in the generalized sense of two
Daniel Schug, Sai Yerramreddy, Rich Caruana, Craig Greenberg, Justyna Zwolak
As the deployment of computer vision technology becomes increasingly common in science, the need for explanations of the system and its output has become a
We have developed a virtual measurement and analysis laboratory in a 3D immersive virtual environment using a CAVE hardware configuration and a software
We have observed a problem with OpenSceneGraph (OSG) in which parallel, multi-threaded, applications do not scale properly to multiple graphics cards. The
