J. Lißner. Dataset, (2020)Related to: Lißner, Julian, and Felix Fritzen. "Data-Driven Microstructure Property Relations." Mathematical and Computational Applications 24.2 (2019): 57. doi: 10.3390/mca24020057.
DOI: 10.18419/darus-1151
Abstract
The hdf5 file contains image data of inclusion based microstructured material and the homogenized effective heat conductivity thereof. The microstructure is defined with a representative volume element with periodic boundary conditions. 30.000 images are contained and split into two inclusion subclasses of circular and rectangular inclusions. Features computed via the 2-point correlation function can be found. The features and effective properties have been used for a regression problem in the related paper. Example code to access the data and recreate the features is attached.
Related to: Lißner, Julian, and Felix Fritzen. "Data-Driven Microstructure Property Relations." Mathematical and Computational Applications 24.2 (2019): 57. doi: 10.3390/mca24020057
%0 Generic
%1 lissner2020microstructure
%A Lißner, Julian
%D 2020
%K darus ubs_10002 ubs_20002 ubs_30024 ubs_40398 unibibliografie
%R 10.18419/darus-1151
%T 2d microstructure data
%X The hdf5 file contains image data of inclusion based microstructured material and the homogenized effective heat conductivity thereof. The microstructure is defined with a representative volume element with periodic boundary conditions. 30.000 images are contained and split into two inclusion subclasses of circular and rectangular inclusions. Features computed via the 2-point correlation function can be found. The features and effective properties have been used for a regression problem in the related paper. Example code to access the data and recreate the features is attached.
@misc{lissner2020microstructure,
abstract = {The hdf5 file contains image data of inclusion based microstructured material and the homogenized effective heat conductivity thereof. The microstructure is defined with a representative volume element with periodic boundary conditions. 30.000 images are contained and split into two inclusion subclasses of circular and rectangular inclusions. Features computed via the 2-point correlation function can be found. The features and effective properties have been used for a regression problem in the related paper. Example code to access the data and recreate the features is attached.},
added-at = {2022-03-08T18:06:16.000+0100},
affiliation = {Lißner, Julian/Universität Stuttgart},
author = {Lißner, Julian},
biburl = {https://puma.ub.uni-stuttgart.de/bibtex/21d6a793823a32d43f73ac8c66239434d/unibiblio},
doi = {10.18419/darus-1151},
howpublished = {Dataset},
interhash = {10e73085ee78b8d83810d45907d0e46b},
intrahash = {1d6a793823a32d43f73ac8c66239434d},
keywords = {darus ubs_10002 ubs_20002 ubs_30024 ubs_40398 unibibliografie},
note = {Related to: Lißner, Julian, and Felix Fritzen. "Data-Driven Microstructure Property Relations." Mathematical and Computational Applications 24.2 (2019): 57. doi: 10.3390/mca24020057},
orcid-numbers = {Lißner, Julian/0000-0002-2286-5211},
timestamp = {2022-06-02T07:24:04.000+0200},
title = {2d microstructure data},
year = 2020
}
