[{"author":[{"full_name":"Voigt, Tim","last_name":"Voigt","first_name":"Tim","id":"220691"},{"last_name":"Kohlhase","orcid":"0009-0002-9374-0720","full_name":"Kohlhase, Martin","first_name":"Martin","id":"226669"},{"first_name":"Oliver","full_name":"Nelles, Oliver","last_name":"Nelles"}],"citation":{"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.","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","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>.","short":"T. Voigt, M. Kohlhase, O. Nelles, Mathematics 9 (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>.","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>"},"status":"public","doi":"10.3390/math9192479","intvolume":"         9","date_updated":"2026-03-17T15:28:49Z","publisher":"MDPI AG","_id":"3717","year":"2021","language":[{"iso":"eng"}],"article_number":"2479","date_created":"2023-11-14T10:52:17Z","title":"Incremental DoE and Modeling Methodology with Gaussian Process Regression: An Industrially Applicable Approach to Incorporate Expert Knowledge","volume":9,"oa":"1","tmp":{"short":"CC BY (4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","image":"/images/cc_by.png"},"publication_identifier":{"eissn":["2227-7390"]},"type":"journal_article","alternative_id":["1477"],"main_file_link":[{"url":"https://www.mdpi.com/2227-7390/9/19/2479","open_access":"1"}],"user_id":"220548","keyword":["Gaussian process regression","design of experiments","static process models","industrial processes","stepwise experimental design"],"issue":"19","publication_status":"published","publication":"Mathematics"}]