We develop an interactive approach for analyzing multi-field tensor data from simulations in close collaboration with domain scientists. Our approach is based on extensive application analysis and built around a multi-field clustering addressing multiple user-defined quantities which were required by the domain scientists. Established techniques like linked views complement the approach to support reasoning while offering an overview and detailed insight into the multi-field tensor data. Further, we include an evaluation containing a real-world use case and a user study with domain scientists to demonstrate the usefulness compared to existing tools.
%0 Conference Paper
%1 10148302
%A Kretzschmar, Vanessa
%A Scheuermann, Gerik
%A Stommel, Markus
%A Gillmann, Christina
%B 2023 IEEE 16th Pacific Visualization Symposium (PacificVis)
%D 2023
%K imported
%P 107-111
%R 10.1109/PacificVis56936.2023.00019
%T A Visual Analytics Inspired Approach to Correlate and Understand Multiple Mechanical Tensor Fields
%X We develop an interactive approach for analyzing multi-field tensor data from simulations in close collaboration with domain scientists. Our approach is based on extensive application analysis and built around a multi-field clustering addressing multiple user-defined quantities which were required by the domain scientists. Established techniques like linked views complement the approach to support reasoning while offering an overview and detailed insight into the multi-field tensor data. Further, we include an evaluation containing a real-world use case and a user study with domain scientists to demonstrate the usefulness compared to existing tools.
@inproceedings{10148302,
abstract = {We develop an interactive approach for analyzing multi-field tensor data from simulations in close collaboration with domain scientists. Our approach is based on extensive application analysis and built around a multi-field clustering addressing multiple user-defined quantities which were required by the domain scientists. Established techniques like linked views complement the approach to support reasoning while offering an overview and detailed insight into the multi-field tensor data. Further, we include an evaluation containing a real-world use case and a user study with domain scientists to demonstrate the usefulness compared to existing tools.},
added-at = {2024-12-10T10:56:34.000+0100},
author = {Kretzschmar, Vanessa and Scheuermann, Gerik and Stommel, Markus and Gillmann, Christina},
biburl = {https://puma.scadsai.uni-leipzig.de/bibtex/2a1bcd2b3371309dc5bf0f43668ee2e14/scadsfct},
booktitle = {2023 IEEE 16th Pacific Visualization Symposium (PacificVis)},
doi = {10.1109/PacificVis56936.2023.00019},
interhash = {5a67a8290841a7e330800db9fff995d0},
intrahash = {a1bcd2b3371309dc5bf0f43668ee2e14},
issn = {2165-8773},
keywords = {imported},
month = {April},
pages = {107-111},
timestamp = {2024-12-10T10:56:34.000+0100},
title = {A Visual Analytics Inspired Approach to Correlate and Understand Multiple Mechanical Tensor Fields},
year = 2023
}
