A methodology for direct parameter identification for experimental results using machine learning — Real world application to the highly non-linear deformation behavior of FRP
J. Gerritzen, A. Hornig, P. Winkler, M. Gude, Computational Materials Science 244 (2024).
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Journal Article
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| English
Author
Gerritzen, JohannesLibreCat 
;
Hornig, Andreas;
Winkler, Peter;
Gude, Maik
;
Hornig, Andreas;
Winkler, Peter;
Gude, MaikProject
Publishing Year
Journal Title
Computational Materials Science
Volume
244
Article Number
113274
ISSN
LibreCat-ID
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Gerritzen J, Hornig A, Winkler P, Gude M. A methodology for direct parameter identification for experimental results using machine learning — Real world application to the highly non-linear deformation behavior of FRP. Computational Materials Science. 2024;244. doi:10.1016/j.commatsci.2024.113274
Gerritzen, J., Hornig, A., Winkler, P., & Gude, M. (2024). A methodology for direct parameter identification for experimental results using machine learning — Real world application to the highly non-linear deformation behavior of FRP. Computational Materials Science, 244, Article 113274. https://doi.org/10.1016/j.commatsci.2024.113274
@article{Gerritzen_Hornig_Winkler_Gude_2024, 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}, DOI={10.1016/j.commatsci.2024.113274}, number={113274}, journal={Computational Materials Science}, publisher={Elsevier BV}, author={Gerritzen, Johannes and Hornig, Andreas and Winkler, Peter and Gude, Maik}, year={2024} }
Gerritzen, Johannes, Andreas Hornig, Peter Winkler, and Maik Gude. “A Methodology for Direct Parameter Identification for Experimental Results Using Machine Learning — Real World Application to the Highly Non-Linear Deformation Behavior of FRP.” Computational Materials Science 244 (2024). https://doi.org/10.1016/j.commatsci.2024.113274.
J. Gerritzen, A. Hornig, P. Winkler, and M. Gude, “A methodology for direct parameter identification for experimental results using machine learning — Real world application to the highly non-linear deformation behavior of FRP,” Computational Materials Science, vol. 244, Art. no. 113274, 2024, doi: 10.1016/j.commatsci.2024.113274.
Gerritzen, Johannes, et al. “A Methodology for Direct Parameter Identification for Experimental Results Using Machine Learning — Real World Application to the Highly Non-Linear Deformation Behavior of FRP.” Computational Materials Science, vol. 244, 113274, Elsevier BV, 2024, doi:10.1016/j.commatsci.2024.113274.
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