Details
| Original language | English |
|---|---|
| Title of host publication | 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 1-8 |
| Number of pages | 8 |
| ISBN (electronic) | 9781538627259 |
| Publication status | Published - 2 Feb 2018 |
| Event | 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Honolulu, United States Duration: 27 Nov 2017 → 1 Dec 2017 |
Publication series
| Name | 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings |
|---|---|
| Volume | 2018-January |
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.
ASJC Scopus subject areas
- Computer Science(all)
- Artificial Intelligence
- Computer Science(all)
- Computer Science Applications
- Mathematics(all)
- Control and Optimization
Cite this
- Standard
- Harvard
- Apa
- Vancouver
- BibTeX
- RIS
2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. p. 1-8 (2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings; Vol. 2018-January).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Comparison of Bayesian and interval uncertainty quantification
T2 - 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017
AU - Broggi, Matteo
AU - Faes, Matthias
AU - Patelli, Edoardo
AU - Govers, Yves
AU - Moens, David
AU - Beer, Michael
N1 - Funding information: 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.
PY - 2018/2/2
Y1 - 2018/2/2
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.
UR - http://www.scopus.com/inward/record.url?scp=85046041174&partnerID=8YFLogxK
U2 - 10.1109/SSCI.2017.8280882
DO - 10.1109/SSCI.2017.8280882
M3 - Conference contribution
T3 - 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings
SP - 1
EP - 8
BT - 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 27 November 2017 through 1 December 2017
ER -