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

Matteo Broggi; Matthias Faes; Edoardo Patelli; Yves Govers; David Moens; Michael Beer

doi:10.1109/SSCI.2017.8280882

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

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

Civil And Environmental Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

21 Citations (Scopus)

34 Downloads (Pure)

Abstract

This paper concerns the comparison of two inverse methods for the quantification of uncertain model parameters, based on experimentally obtained measurement data of the model's responses. Specifically, Bayesian inference is compared to a novel method for the quantification of multivariate interval uncertainty. The 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. Since computational cost of the application of both methods to high-dimensional problems and realistic numerical models can become intractable, an Artificial Neural Network surrogate is used for both methods. The application of this ANN proves to limit the computational cost to a large extent, even taking the generation of the training dataset into account. Concerning the comparison of both methods, 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.

Original language	English
Title of host publication	2017 IEEE Symposium Series on Computational Intelligence
Place of Publication	Piscataway, NJ
Number of pages	8
DOIs	https://doi.org/10.1109/SSCI.2017.8280882
Publication status	Published - 5 Feb 2018
Event	2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Honolulu, United States Duration: 27 Nov 2017 → 1 Dec 2017

Conference

Conference	2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017
Country/Territory	United States
City	Honolulu
Period	27/11/17 → 1/12/17

Funding

ACKNOWLEDGEMENTS Matthias Faes would like to acknowledge the financial support of the Flemish Research Foundation (FWO) in the frame of travel grants K218117N and K217917N for staying at the Leibniz University in Hannover. Matteo Broggi and Matthias Faes contributed equally to this paper.

Keywords

AIRMOD measurement data set
uncertain model parameters
Bayesian identification

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10.1109/SSCI.2017.8280882

Broggi-etal-SSCI2017-Comparison-of-Bayesian-and-interval-uncertainty-quantificationAccepted author manuscript, 1.9 MB

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abstract = "This paper concerns the comparison of two inverse methods for the quantification of uncertain model parameters, based on experimentally obtained measurement data of the model's responses. Specifically, Bayesian inference is compared to a novel method for the quantification of multivariate interval uncertainty. The 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. Since computational cost of the application of both methods to high-dimensional problems and realistic numerical models can become intractable, an Artificial Neural Network surrogate is used for both methods. The application of this ANN proves to limit the computational cost to a large extent, even taking the generation of the training dataset into account. Concerning the comparison of both methods, 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.",

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Broggi, M, Faes, M, Patelli, E, Govers, Y, Moens, D & Beer, M 2018, Comparison of Bayesian and interval uncertainty quantification: application to the AIRMOD test structure. in 2017 IEEE Symposium Series on Computational Intelligence. Piscataway, NJ, 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017, Honolulu, United States, 27/11/17. https://doi.org/10.1109/SSCI.2017.8280882

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AU - Moens, David

AU - Beer, Michael

N1 - © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

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N2 - This paper concerns the comparison of two inverse methods for the quantification of uncertain model parameters, based on experimentally obtained measurement data of the model's responses. Specifically, Bayesian inference is compared to a novel method for the quantification of multivariate interval uncertainty. The 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. Since computational cost of the application of both methods to high-dimensional problems and realistic numerical models can become intractable, an Artificial Neural Network surrogate is used for both methods. The application of this ANN proves to limit the computational cost to a large extent, even taking the generation of the training dataset into account. Concerning the comparison of both methods, 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.

AB - This paper concerns the comparison of two inverse methods for the quantification of uncertain model parameters, based on experimentally obtained measurement data of the model's responses. Specifically, Bayesian inference is compared to a novel method for the quantification of multivariate interval uncertainty. The 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. Since computational cost of the application of both methods to high-dimensional problems and realistic numerical models can become intractable, an Artificial Neural Network surrogate is used for both methods. The application of this ANN proves to limit the computational cost to a large extent, even taking the generation of the training dataset into account. Concerning the comparison of both methods, 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.

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

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