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%0 Journal Article
%1 rehme2023uncertainty
%A Rehme, Michael F.
%A John, David N.
%A Schick, Michael
%A Pflüger, Dirk
%D 2023
%I Elsevier
%J Mechatronics
%K
%P 102989
%R 10.1016/j.mechatronics.2023.102989
%T Uncertainty Quantification for parameter estimation of an industrial electric motor using hierarchical Bayesian inversion
%V 92
@article{rehme2023uncertainty,
added-at = {2023-12-21T15:02:47.000+0100},
affiliation = {Rehme, MF (Corresponding Author), Univ Stuttgart, Inst Parallel & Distributed Syst, D-70569 Stuttgart, Germany.
Rehme, Michael F.; Pflueger, Dirk, Univ Stuttgart, Inst Parallel & Distributed Syst, D-70569 Stuttgart, Germany.
John, David N.; Schick, Michael, Bosch Res, D-71272 Renningen, Germany.},
author = {Rehme, Michael F. and John, David N. and Schick, Michael and Pflüger, Dirk},
biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2430d1a86111ea145b4b074a30c952e43/unibiblio},
doi = {10.1016/j.mechatronics.2023.102989},
interhash = {35874047c0b08e1f70624d764c100664},
intrahash = {430d1a86111ea145b4b074a30c952e43},
issn = {{0957-4158} and {1873-4006}},
journal = {Mechatronics},
keywords = {},
language = {eng},
pages = 102989,
publisher = {Elsevier},
research-areas = {Automation & Control Systems; Engineering; Robotics},
timestamp = {2023-12-21T14:02:47.000+0100},
title = {Uncertainty Quantification for parameter estimation of an industrial electric motor using hierarchical Bayesian inversion},
unique-id = {WOS:000990990500001},
volume = 92,
year = 2023
}