Sensitivity-guided iterative parameter identification and data generation with BayesFlow and PELS-VAE for model calibration

  • Calibration of complex system models with a large number of parameters using standard optimization methods is often extremely time-consuming and not fully automated due to the reliance on all-inclusive expert knowledge. We propose a sensitivity-guided iterative parameter identification and data generation algorithm. The sensitivity analysis replaces manual intervention, the parameter identification is realized by BayesFlow allowing for uncertainty quantification, and the data generation with the physics-enhanced latent space variational autoencoder (PELS-VAE) between two iteration steps enables inference of weakly identifiable parameters. A complete calibration experiment was conducted on a thermal model of an automotive cabin. The average relative error rate over all parameter estimates of 1.62% and the mean absolute error of calibrated model outputs of 0.108∘C validate the feasibility and effectiveness of the method. Moreover, the entire procedure accelerates up to 1 day, whereas theCalibration of complex system models with a large number of parameters using standard optimization methods is often extremely time-consuming and not fully automated due to the reliance on all-inclusive expert knowledge. We propose a sensitivity-guided iterative parameter identification and data generation algorithm. The sensitivity analysis replaces manual intervention, the parameter identification is realized by BayesFlow allowing for uncertainty quantification, and the data generation with the physics-enhanced latent space variational autoencoder (PELS-VAE) between two iteration steps enables inference of weakly identifiable parameters. A complete calibration experiment was conducted on a thermal model of an automotive cabin. The average relative error rate over all parameter estimates of 1.62% and the mean absolute error of calibrated model outputs of 0.108∘C validate the feasibility and effectiveness of the method. Moreover, the entire procedure accelerates up to 1 day, whereas the classical calibration method takes more than 1 week.show moreshow less

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Metadaten
Author:Yi Zhang, Lars MikelsonsGND
URN:urn:nbn:de:bvb:384-opus4-1063470
Frontdoor URLhttps://opus.bibliothek.uni-augsburg.de/opus4/106347
ISSN:2213-7467OPAC
Parent Title (English):Advanced Modeling and Simulation in Engineering Sciences
Publisher:Springer
Place of publication:Berlin
Type:Article
Language:English
Year of first Publication:2023
Publishing Institution:Universität Augsburg
Release Date:2023/07/25
Tag:Applied Mathematics; Computer Science Applications; Engineering (miscellaneous); Modeling and Simulation
Volume:10
Issue:1
First Page:9
DOI:https://doi.org/10.1186/s40323-023-00246-y
Institutes:Fakultät für Angewandte Informatik
Fakultät für Angewandte Informatik / Institut für Informatik
Fakultät für Angewandte Informatik / Institut für Informatik / Lehrstuhl für Ingenieurinformatik mit Schwerpunkt Mechatronik
Dewey Decimal Classification:6 Technik, Medizin, angewandte Wissenschaften / 60 Technik / 600 Technik, Technologie
Licence (German):CC-BY 4.0: Creative Commons: Namensnennung (mit Print on Demand)