Details
| Original language | English |
|---|---|
| Pages (from-to) | 534-548 |
| Number of pages | 15 |
| Journal | Mechanical Systems and Signal Processing |
| Volume | 118 |
| Early online date | 11 Sept 2018 |
| Publication status | Published - 1 Mar 2019 |
Abstract
This paper introduces an improved version of a novel inverse approach for the quantification of multivariate interval uncertainty for high dimensional models under scarce data availability. Furthermore, a conceptual and practical comparison of the method with the well-established probabilistic framework of Bayesian model updating via Transitional Markov Chain Monte Carlo is presented in the context of the DLR-AIRMOD test structure. First, it is shown that the proposed improvements of the inverse method alleviate the curse of dimensionality of the method with a factor up to 105. Furthermore, the comparison with the Bayesian results revealed that the selection ofthe most appropriate method depends largely on the desired information and availability of data. In case large amounts of data are available, and/or the analyst desires full (joint)-probabilistic descriptors of the model parameter uncertainty, the Bayesian method is shown to be the most performing. On the other hand however, when such descriptors are not needed (e.g., for worst-case analysis), and only scarce data are available, the interval method is shown to deliver more objective and robust bounds on the uncertain parameters. Finally, also suggestions to aid the analyst in selecting the most appropriate method for inverse uncertainty quantification are given.
Keywords
- Bayesian model updating, DLR-AIRMOD, Limited data, Multivariate interval uncertainty, Uncertainty quantification
ASJC Scopus subject areas
- Engineering(all)
- Control and Systems Engineering
- Computer Science(all)
- Signal Processing
- Engineering(all)
- Civil and Structural Engineering
- Engineering(all)
- Aerospace Engineering
- Engineering(all)
- Mechanical Engineering
- Computer Science(all)
- Computer Science Applications
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In: Mechanical Systems and Signal Processing, Vol. 118, 01.03.2019, p. 534-548.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - A multivariate interval approach for inverse uncertainty quantification with limited experimental data
AU - Faes, Matthias
AU - Broggi, Matteo
AU - Patelli, Edoardo
AU - Govers, Yves
AU - Mottershead, John
AU - Beer, Michael
AU - Moens, David
N1 - Funding Information: The authors would like to acknowledge the financial support of the Flemish Research Foundation (FWO) for travel Grants K218117N and K217917N and the research project G0C2218N.
PY - 2019/3/1
Y1 - 2019/3/1
N2 - This paper introduces an improved version of a novel inverse approach for the quantification of multivariate interval uncertainty for high dimensional models under scarce data availability. Furthermore, a conceptual and practical comparison of the method with the well-established probabilistic framework of Bayesian model updating via Transitional Markov Chain Monte Carlo is presented in the context of the DLR-AIRMOD test structure. First, it is shown that the proposed improvements of the inverse method alleviate the curse of dimensionality of the method with a factor up to 105. Furthermore, the comparison with the Bayesian results revealed that the selection ofthe most appropriate method depends largely on the desired information and availability of data. In case large amounts of data are available, and/or the analyst desires full (joint)-probabilistic descriptors of the model parameter uncertainty, the Bayesian method is shown to be the most performing. On the other hand however, when such descriptors are not needed (e.g., for worst-case analysis), and only scarce data are available, the interval method is shown to deliver more objective and robust bounds on the uncertain parameters. Finally, also suggestions to aid the analyst in selecting the most appropriate method for inverse uncertainty quantification are given.
AB - This paper introduces an improved version of a novel inverse approach for the quantification of multivariate interval uncertainty for high dimensional models under scarce data availability. Furthermore, a conceptual and practical comparison of the method with the well-established probabilistic framework of Bayesian model updating via Transitional Markov Chain Monte Carlo is presented in the context of the DLR-AIRMOD test structure. First, it is shown that the proposed improvements of the inverse method alleviate the curse of dimensionality of the method with a factor up to 105. Furthermore, the comparison with the Bayesian results revealed that the selection ofthe most appropriate method depends largely on the desired information and availability of data. In case large amounts of data are available, and/or the analyst desires full (joint)-probabilistic descriptors of the model parameter uncertainty, the Bayesian method is shown to be the most performing. On the other hand however, when such descriptors are not needed (e.g., for worst-case analysis), and only scarce data are available, the interval method is shown to deliver more objective and robust bounds on the uncertain parameters. Finally, also suggestions to aid the analyst in selecting the most appropriate method for inverse uncertainty quantification are given.
KW - Bayesian model updating
KW - DLR-AIRMOD
KW - Limited data
KW - Multivariate interval uncertainty
KW - Uncertainty quantification
UR - http://www.scopus.com/inward/record.url?scp=85053181074&partnerID=8YFLogxK
U2 - 10.1016/j.ymssp.2018.08.050
DO - 10.1016/j.ymssp.2018.08.050
M3 - Article
AN - SCOPUS:85053181074
VL - 118
SP - 534
EP - 548
JO - Mechanical Systems and Signal Processing
JF - Mechanical Systems and Signal Processing
SN - 0888-3270
ER -