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Comparison between Bayesian and interval uncertainty quantification: application to the AIRMOD test structure

Broggi, Mateo and Faes, Matthias and Patelli, E. and Govers, Yves and Moens, David and Beer, Michael (2017) Comparison between Bayesian and interval uncertainty quantification: application to the AIRMOD test structure. In: IEEE SSCI 2017 - Symposium Series on Computational Intelligence. IEEE SSCI 2017 - Symposium Series on Computational Intelligence, 27. Nov. - 01. Dez. 2017, Honululu, Hawaii, USA.

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Abstract

This paper concerns the comparison of two inverse methods for the quantification of uncertain model parameters, based on independent measurement data of the model’s responses. Specifically, Bayesian inference is compared to a novel method for the quantification of multivariate interval uncertainty. This comparison is made by applying both methods to the AIRMOD measurement data set, and comparing their results critically in terms of obtained information and computational expense. It is found that the results of the Bayesian identification provide less over-conservative bounds on the uncertainty in the responses of the AIRMOD model. Smthing about computational cost.

Item URL in elib:https://elib.dlr.de/115579/
Document Type:Conference or Workshop Item (Speech)
Title:Comparison between Bayesian and interval uncertainty quantification: application to the AIRMOD test structure
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Broggi, MateoLeibniz University HannoverUNSPECIFIED
Faes, MatthiasKU LeuvenUNSPECIFIED
Patelli, E.University of LiverpoolUNSPECIFIED
Govers, Yvesyves.govers (at) dlr.dehttps://orcid.org/0000-0003-2236-596X
Moens, DavidKU LeuvenUNSPECIFIED
Beer, MichaelLeibniz University HannoverUNSPECIFIED
Date:2017
Journal or Publication Title:IEEE SSCI 2017 - Symposium Series on Computational Intelligence
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Status:Published
Keywords:interval uncertainty, airmod test structure, parameters, bayesian
Event Title:IEEE SSCI 2017 - Symposium Series on Computational Intelligence
Event Location:Honululu, Hawaii, USA
Event Type:international Conference
Event Dates:27. Nov. - 01. Dez. 2017
Organizer:IEEE Computational Intelligence Society
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Aeronautics
HGF - Program Themes:fixed-wing aircraft
DLR - Research area:Aeronautics
DLR - Program:L AR - Aircraft Research
DLR - Research theme (Project):L - Flight Physics
Location: Göttingen
Institutes and Institutions:Institute of Aeroelasticity > Structural Dynamics and System Identification
Deposited By: Grischke, Birgid
Deposited On:06 Dec 2017 15:47
Last Modified:09 Feb 2018 11:40

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