[{"type":"journal_article","date_created":"2025-11-04T12:37:42Z","project":[{"name":"TRR 285:  Methodenentwicklung zur mechanischen Fügbarkeit in wandlungsfähigen Prozessketten","_id":"130"},{"name":"TRR 285 - Subproject A03","_id":"137"},{"_id":"131","name":"TRR 285 - Project Area A"}],"publication":"Computational Materials Science","citation":{"ama":"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. <i>Computational Materials Science</i>. 2024;244. doi:<a href=\"https://doi.org/10.1016/j.commatsci.2024.113274\">10.1016/j.commatsci.2024.113274</a>","bibtex":"@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={<a href=\"https://doi.org/10.1016/j.commatsci.2024.113274\">10.1016/j.commatsci.2024.113274</a>}, number={113274}, journal={Computational Materials Science}, publisher={Elsevier BV}, author={Gerritzen, Johannes and Hornig, Andreas and Winkler, Peter and Gude, Maik}, year={2024} }","mla":"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.” <i>Computational Materials Science</i>, vol. 244, 113274, Elsevier BV, 2024, doi:<a href=\"https://doi.org/10.1016/j.commatsci.2024.113274\">10.1016/j.commatsci.2024.113274</a>.","chicago":"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.” <i>Computational Materials Science</i> 244 (2024). <a href=\"https://doi.org/10.1016/j.commatsci.2024.113274\">https://doi.org/10.1016/j.commatsci.2024.113274</a>.","short":"J. Gerritzen, A. Hornig, P. Winkler, M. Gude, Computational Materials Science 244 (2024).","apa":"Gerritzen, J., Hornig, A., Winkler, P., &#38; 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. <i>Computational Materials Science</i>, <i>244</i>, Article 113274. <a href=\"https://doi.org/10.1016/j.commatsci.2024.113274\">https://doi.org/10.1016/j.commatsci.2024.113274</a>","ieee":"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,” <i>Computational Materials Science</i>, vol. 244, Art. no. 113274, 2024, doi: <a href=\"https://doi.org/10.1016/j.commatsci.2024.113274\">10.1016/j.commatsci.2024.113274</a>."},"user_id":"105344","doi":"10.1016/j.commatsci.2024.113274","volume":244,"article_number":"113274","_id":"62076","publisher":"Elsevier BV","language":[{"iso":"eng"}],"publication_status":"published","date_updated":"2026-02-27T06:46:35Z","intvolume":"       244","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","status":"public","year":"2024","publication_identifier":{"issn":["0927-0256"]},"author":[{"id":"105344","full_name":"Gerritzen, Johannes","last_name":"Gerritzen","orcid":"0000-0002-0169-8602","first_name":"Johannes"},{"last_name":"Hornig","first_name":"Andreas","full_name":"Hornig, Andreas"},{"first_name":"Peter","last_name":"Winkler","full_name":"Winkler, Peter"},{"full_name":"Gude, Maik","last_name":"Gude","first_name":"Maik"}]}]