A methodology for direct parameter identification for experimental results using machine learning — Real world application to the highly non-linear deformation behavior of FRP

article A methodology for direct parameter identification for experimental results using machine learning — Real world application to the highly non-linear deformation behavior of FRP published Johannes Gerritzen author 1053440000-0002-0169-8602 Andreas Hornig author Peter Winkler author Maik Gude author TRR 285: Methodenentwicklung zur mechanischen Fügbarkeit in wandlungsfähigen Prozessketten project TRR 285 - Subproject A03 project TRR 285 - Project Area A project Elsevier BV2024 eng Computational Materials Science 0927-025610.1016/j.commatsci.2024.113274 244 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). <a href="https://doi.org/10.1016/j.commatsci.2024.113274">https://doi.org/10.1016/j.commatsci.2024.113274</a>. J. Gerritzen, A. Hornig, P. Winkler, M. Gude, Computational Materials Science 244 (2024). 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. <a href="https://doi.org/10.1016/j.commatsci.2024.113274">https://doi.org/10.1016/j.commatsci.2024.113274</a> 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: <a href="https://doi.org/10.1016/j.commatsci.2024.113274">10.1016/j.commatsci.2024.113274</a>. 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:<a href="https://doi.org/10.1016/j.commatsci.2024.113274">10.1016/j.commatsci.2024.113274</a> @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} } 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:<a href="https://doi.org/10.1016/j.commatsci.2024.113274">10.1016/j.commatsci.2024.113274</a>. 620762025-11-04T12:37:42Z2026-02-27T06:46:35Z