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Inverse quantification of epistemic uncertainty under scarce data: Bayesian or interval approach?

Faes, Matthias and Broggi, Matteo and Patelli, Edoardo and Govers, Yves and Mottershead, John and Beer, Michael and Moens, David (2019) Inverse quantification of epistemic uncertainty under scarce data: Bayesian or interval approach? In: 13th International Conference on Applications of Statistics and Probability in Civil Engineering (ICASP13), pp. 226-233. ICASP13 - 13th International Conference on Applications of Statistics and Probability in Civil Engineering, 26.-30. Mai 2019, Seoul, South Korea. DOI: 10.22725/ICASP13.060 ISBN 979-11-967125-0-1

Full text not available from this repository.

Official URL: http://hdl.handle.net/10371/153281

Abstract

This paper introduces a practical comparison of a newly introduced inverse method for the quantification of epistemically uncertain model parameters with the well-established probabilistic framework of Bayesian model updating via Transitional Markov Chain Monte Carlo. The paper gives a concise overview of both techniques, and both methods are applied to the quantification of a set of parameters in the well-known DLR Airmod test structure. Specifically, the case where only a very scarce set of experimentally obtained eigenfrequencies and eigenmodes are available is considered. It is shown that for such scarce data, the interval method provides more objective and robust bounds on the uncertain parameters than the Bayesian method, since no prior definition of the uncertainty is required, albeit at the cost that less information on parameter dependency or relative plausibility of different parameter values is obtained.

Item URL in elib:https://elib.dlr.de/127630/
Document Type:Conference or Workshop Item (Speech)
Title:Inverse quantification of epistemic uncertainty under scarce data: Bayesian or interval approach?
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Faes, Matthiasmatthias.faes (at) kuleuven.beUNSPECIFIED
Broggi, Matteobroggi (at) irz.uni-hannover.deUNSPECIFIED
Patelli, EdoardoEdoardo.Patelli (at) liverpool.ac.ukUNSPECIFIED
Govers, YvesYves.Govers (at) dlr.dehttps://orcid.org/0000-0003-2236-596X
Mottershead, Johnj.e.mottershead (at) liverpool.ac.ukUNSPECIFIED
Beer, Michaelbeer (at) irz.uni-hannover.deUNSPECIFIED
Moens, Daviddavid.moens (at) kuleuven.beUNSPECIFIED
Date:26 May 2019
Journal or Publication Title:13th International Conference on Applications of Statistics and Probability in Civil Engineering (ICASP13)
Refereed publication:Yes
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
DOI :10.22725/ICASP13.060
Page Range:pp. 226-233
ISBN:979-11-967125-0-1
Status:Published
Keywords:uncertain model parameters, Bayesian model updating, DLR AIRMOD structure
Event Title:ICASP13 - 13th International Conference on Applications of Statistics and Probability in Civil Engineering
Event Location:Seoul, South Korea
Event Type:international Conference
Event Dates:26.-30. Mai 2019
Organizer:Seoul National University
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Aeronautics
HGF - Program Themes:other
DLR - Research area:Aeronautics
DLR - Program:L - no assignment
DLR - Research theme (Project):L - no assignment
Location: Göttingen
Institutes and Institutions:Institute of Aeroelasticity > Structural Dynamics and System Identification
Deposited By: Govers, Dr.-Ing. Yves
Deposited On:06 Jun 2019 09:26
Last Modified:06 Jun 2019 09:40

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