---
_id: '3717'
alternative_id:
- '1477'
article_number: '2479'
author:
- first_name: Tim
  full_name: Voigt, Tim
  id: '220691'
  last_name: Voigt
- first_name: Martin
  full_name: Kohlhase, Martin
  id: '226669'
  last_name: Kohlhase
  orcid: 0009-0002-9374-0720
- first_name: Oliver
  full_name: Nelles, Oliver
  last_name: Nelles
citation:
  alphadin: '<span style="font-variant:small-caps;">Voigt, Tim</span> ; <span style="font-variant:small-caps;">Kohlhase,
    Martin</span> ; <span style="font-variant:small-caps;">Nelles, Oliver</span>:
    Incremental DoE and Modeling Methodology with Gaussian Process Regression: An
    Industrially Applicable Approach to Incorporate Expert Knowledge. In: <i>Mathematics</i>
    Bd. 9, MDPI AG (2021), Nr. 19'
  ama: 'Voigt T, Kohlhase M, Nelles O. Incremental DoE and Modeling Methodology with
    Gaussian Process Regression: An Industrially Applicable Approach to Incorporate
    Expert Knowledge. <i>Mathematics</i>. 2021;9(19). doi:<a href="https://doi.org/10.3390/math9192479">10.3390/math9192479</a>'
  apa: 'Voigt, T., Kohlhase, M., &#38; Nelles, O. (2021). Incremental DoE and Modeling
    Methodology with Gaussian Process Regression: An Industrially Applicable Approach
    to Incorporate Expert Knowledge. <i>Mathematics</i>, <i>9</i>(19). <a href="https://doi.org/10.3390/math9192479">https://doi.org/10.3390/math9192479</a>'
  bibtex: '@article{Voigt_Kohlhase_Nelles_2021, title={Incremental DoE and Modeling
    Methodology with Gaussian Process Regression: An Industrially Applicable Approach
    to Incorporate Expert Knowledge}, volume={9}, DOI={<a href="https://doi.org/10.3390/math9192479">10.3390/math9192479</a>},
    number={192479}, journal={Mathematics}, publisher={MDPI AG}, author={Voigt, Tim
    and Kohlhase, Martin and Nelles, Oliver}, year={2021} }'
  chicago: 'Voigt, Tim, Martin Kohlhase, and Oliver Nelles. “Incremental DoE and Modeling
    Methodology with Gaussian Process Regression: An Industrially Applicable Approach
    to Incorporate Expert Knowledge.” <i>Mathematics</i> 9, no. 19 (2021). <a href="https://doi.org/10.3390/math9192479">https://doi.org/10.3390/math9192479</a>.'
  ieee: 'T. Voigt, M. Kohlhase, and O. Nelles, “Incremental DoE and Modeling Methodology
    with Gaussian Process Regression: An Industrially Applicable Approach to Incorporate
    Expert Knowledge,” <i>Mathematics</i>, vol. 9, no. 19, 2021.'
  mla: 'Voigt, Tim, et al. “Incremental DoE and Modeling Methodology with Gaussian
    Process Regression: An Industrially Applicable Approach to Incorporate Expert
    Knowledge.” <i>Mathematics</i>, vol. 9, no. 19, 2479, MDPI AG, 2021, doi:<a href="https://doi.org/10.3390/math9192479">10.3390/math9192479</a>.'
  short: T. Voigt, M. Kohlhase, O. Nelles, Mathematics 9 (2021).
date_created: 2023-11-14T10:52:17Z
date_updated: 2026-03-17T15:28:49Z
doi: 10.3390/math9192479
intvolume: '         9'
issue: '19'
keyword:
- Gaussian process regression
- design of experiments
- static process models
- industrial processes
- stepwise experimental design
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://www.mdpi.com/2227-7390/9/19/2479
oa: '1'
publication: Mathematics
publication_identifier:
  eissn:
  - 2227-7390
publication_status: published
publisher: MDPI AG
status: public
title: 'Incremental DoE and Modeling Methodology with Gaussian Process Regression:
  An Industrially Applicable Approach to Incorporate Expert Knowledge'
tmp:
  image: /images/cc_by.png
  legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
  name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
  short: CC BY (4.0)
type: journal_article
user_id: '220548'
volume: 9
year: '2021'
...