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

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

15 Citations (Scopus)
1 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 languageEnglish
Title of host publication2017 IEEE Symposium Series on Computational Intelligence
Place of PublicationPiscataway, NJ
Number of pages8
DOIs
Publication statusPublished - 5 Feb 2018
Event2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Honolulu, United States
Duration: 27 Nov 20171 Dec 2017

Conference

Conference2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017
CountryUnited States
CityHonolulu
Period27/11/171/12/17

Keywords

  • AIRMOD measurement data set
  • uncertain model parameters
  • Bayesian identification

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