---
_id: '62076'
article_number: '113274'
author:
- first_name: Johannes
  full_name: Gerritzen, Johannes
  id: '105344'
  last_name: Gerritzen
  orcid: 0000-0002-0169-8602
- first_name: Andreas
  full_name: Hornig, Andreas
  last_name: Hornig
- first_name: Peter
  full_name: Winkler, Peter
  last_name: Winkler
- first_name: Maik
  full_name: Gude, Maik
  last_name: Gude
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>
  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>
  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} }'
  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>.
  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>.'
  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>.
  short: J. Gerritzen, A. Hornig, P. Winkler, M. Gude, Computational Materials Science
    244 (2024).
date_created: 2025-11-04T12:37:42Z
date_updated: 2026-02-27T06:46:35Z
doi: 10.1016/j.commatsci.2024.113274
intvolume: '       244'
language:
- iso: eng
project:
- _id: '130'
  name: 'TRR 285:  Methodenentwicklung zur mechanischen Fügbarkeit in wandlungsfähigen
    Prozessketten'
- _id: '137'
  name: TRR 285 - Subproject A03
- _id: '131'
  name: TRR 285 - Project Area A
publication: Computational Materials Science
publication_identifier:
  issn:
  - 0927-0256
publication_status: published
publisher: Elsevier BV
status: public
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
type: journal_article
user_id: '105344'
volume: 244
year: '2024'
...