A methodology for direct parameter identification for experimental results using machine learning—Real world application to the highly non-linear deformation behavior of FRP
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%0 Journal Article
%1 gerritzen2024methodology
%A Gerritzen, Johannes
%A Hornig, Andreas
%A Winkler, Peter
%A Gude, Maik
%D 2024
%I Elsevier
%J Computational Materials Science
%K topic_engineering area_architectures FRP using experimental deformation direct learning application identification world parameter machine Real results highly non-linear behavior
%P 113274
%T A methodology for direct parameter identification for experimental results using machine learning—Real world application to the highly non-linear deformation behavior of FRP
%V 244
@article{gerritzen2024methodology,
added-at = {2024-11-14T10:45:35.000+0100},
author = {Gerritzen, Johannes and Hornig, Andreas and Winkler, Peter and Gude, Maik},
biburl = {https://puma.scadsai.uni-leipzig.de/bibtex/24ea01a456bf101200186bd54e9fd9c00/scadsfct},
interhash = {e45da739ed33fc18e4c9e48dd9c9e9bc},
intrahash = {4ea01a456bf101200186bd54e9fd9c00},
journal = {Computational Materials Science},
keywords = {topic_engineering area_architectures FRP using experimental deformation direct learning application identification world parameter machine Real results highly non-linear behavior},
pages = 113274,
publisher = {Elsevier},
timestamp = {2024-11-22T15:56:47.000+0100},
title = {A methodology for direct parameter identification for experimental results using machine learning—Real world application to the highly non-linear deformation behavior of FRP},
volume = 244,
year = 2024
}
