Abstract
The Delaunay density diagnostic helps detect and assess geometric scales within unstructured numerical data sets. The algorithm was first introduced in the paper "Data-driven geometric scale detection via Delaunay interpolation" by Andrew Gillette and Eugene Kur, 2022. More details can be found in that paper, as well as the included README file.
- Developers:
-
Gillette, Andrew [1]
- Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
- Release Date:
- 2022-03-09
- Project Type:
- Open Source, Publicly Available Repository
- Software Type:
- Scientific
- Version:
- 1.0
- Licenses:
-
MIT License
- Sponsoring Org.:
-
USDOE National Nuclear Security Administration (NNSA)Primary Award/Contract Number:AC52-07NA27344
- Code ID:
- 72093
- Site Accession Number:
- LLNL-CODE-833036
- Research Org.:
- Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
- Country of Origin:
- United States
Citation Formats
Gillette, Andrew K.
Delaunay density diagnostic.
Computer Software.
https://github.com/LLNL/ddd.
USDOE National Nuclear Security Administration (NNSA).
09 Mar. 2022.
Web.
doi:10.11578/dc.20220324.3.
Gillette, Andrew K.
(2022, March 09).
Delaunay density diagnostic.
[Computer software].
https://github.com/LLNL/ddd.
https://doi.org/10.11578/dc.20220324.3.
Gillette, Andrew K.
"Delaunay density diagnostic." Computer software.
March 09, 2022.
https://github.com/LLNL/ddd.
https://doi.org/10.11578/dc.20220324.3.
@misc{
doecode_72093,
title = {Delaunay density diagnostic},
author = {Gillette, Andrew K.},
abstractNote = {The Delaunay density diagnostic helps detect and assess geometric scales within unstructured numerical data sets. The algorithm was first introduced in the paper "Data-driven geometric scale detection via Delaunay interpolation" by Andrew Gillette and Eugene Kur, 2022. More details can be found in that paper, as well as the included README file.},
doi = {10.11578/dc.20220324.3},
url = {https://doi.org/10.11578/dc.20220324.3},
howpublished = {[Computer Software] \url{https://doi.org/10.11578/dc.20220324.3}},
year = {2022},
month = {mar}
}