A multivariate interval approach for inverse uncertainty quantification with limited experimental data

Matthias Faes, Matteo Broggi, Edoardo Patelli, Yves Govers, John Mottershead, Michael Beer, David Moens

Research output: Contribution to journalArticle

18 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)534-548
Number of pages15
JournalMechanical Systems and Signal Processing
Volume118
Early online date11 Sep 2018
DOIs
Publication statusPublished - 1 Mar 2019

    Fingerprint

Keywords

  • Bayesian model updating
  • DLR-AIRMOD
  • limited data
  • multivariate interval uncertainty
  • uncertainty quantification

Cite this