%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 }