Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Proceedings of the 8th International Symposium on Reliability Engineering and Risk Management, ISRERM 2022 |
| Herausgeber/-innen | Michael Beer, Enrico Zio, Kok-Kwang Phoon, Bilal M. Ayyub |
| Seiten | 160-165 |
| Seitenumfang | 6 |
| Publikationsstatus | Veröffentlicht - Sept. 2022 |
| Veranstaltung | 8th International Symposium on Reliability Engineering and Risk Management, ISRERM 2022 - Hannover, Deutschland Dauer: 4 Sept. 2022 → 7 Sept. 2022 |
Publikationsreihe
| Name | Proceedings of the 8th International Symposium on Reliability Engineering and Risk Management, ISRERM 2022 |
|---|
Abstract
Multiple types of uncertainty characterization models usually coexist within a single practical uncertainty quantification (UQ) problem. However, efficient propagation of such hybrid uncertainties still remains one of the biggest computational challenges to be tackled in the UQ community. In this study, a novel Bayesian approach, termed ‘Parallel Bayesian Quadrature Optimization’ (PBQO), is proposed to estimate the response expectation function (REF) under hybrid uncertainties in the form of probability models, parametric p-box models and interval models. By assigning a Gaussian process (GP) prior over the augmented (transformed) response function, the posterior distribution of the REF w.r.t. interval parameters is also proven to be a GP. The posterior mean and variance functions of the induced GP are derived in closed form. Besides, a novel strategy is proposed to select multiple points at each iteration so as to take advantage of parallel computing. The efficiency and accuracy of the proposed method is demonstrated by a numerical example.
ASJC Scopus Sachgebiete
- Entscheidungswissenschaften (insg.)
- Managementlehre und Operations Resarch
- Ingenieurwesen (insg.)
- Sicherheit, Risiko, Zuverlässigkeit und Qualität
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- BibTex
- RIS
Proceedings of the 8th International Symposium on Reliability Engineering and Risk Management, ISRERM 2022. Hrsg. / Michael Beer; Enrico Zio; Kok-Kwang Phoon; Bilal M. Ayyub. 2022. S. 160-165 (Proceedings of the 8th International Symposium on Reliability Engineering and Risk Management, ISRERM 2022).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Estimation of response expectation function under hybrid uncertainties by parallel Bayesian quadrature optimization
AU - Dang, C.
AU - Wei, P.
AU - Faes, M.
AU - Beer, M.
PY - 2022/9
Y1 - 2022/9
N2 - Multiple types of uncertainty characterization models usually coexist within a single practical uncertainty quantification (UQ) problem. However, efficient propagation of such hybrid uncertainties still remains one of the biggest computational challenges to be tackled in the UQ community. In this study, a novel Bayesian approach, termed ‘Parallel Bayesian Quadrature Optimization’ (PBQO), is proposed to estimate the response expectation function (REF) under hybrid uncertainties in the form of probability models, parametric p-box models and interval models. By assigning a Gaussian process (GP) prior over the augmented (transformed) response function, the posterior distribution of the REF w.r.t. interval parameters is also proven to be a GP. The posterior mean and variance functions of the induced GP are derived in closed form. Besides, a novel strategy is proposed to select multiple points at each iteration so as to take advantage of parallel computing. The efficiency and accuracy of the proposed method is demonstrated by a numerical example.
AB - Multiple types of uncertainty characterization models usually coexist within a single practical uncertainty quantification (UQ) problem. However, efficient propagation of such hybrid uncertainties still remains one of the biggest computational challenges to be tackled in the UQ community. In this study, a novel Bayesian approach, termed ‘Parallel Bayesian Quadrature Optimization’ (PBQO), is proposed to estimate the response expectation function (REF) under hybrid uncertainties in the form of probability models, parametric p-box models and interval models. By assigning a Gaussian process (GP) prior over the augmented (transformed) response function, the posterior distribution of the REF w.r.t. interval parameters is also proven to be a GP. The posterior mean and variance functions of the induced GP are derived in closed form. Besides, a novel strategy is proposed to select multiple points at each iteration so as to take advantage of parallel computing. The efficiency and accuracy of the proposed method is demonstrated by a numerical example.
KW - Experimental design
KW - Gaussian process
KW - Hybrid uncertainties
KW - Parallel computing
KW - Response expectation function
UR - http://www.scopus.com/inward/record.url?scp=85202009332&partnerID=8YFLogxK
U2 - 10.3850/978-981-18-5184-1_MS-06-123-cd
DO - 10.3850/978-981-18-5184-1_MS-06-123-cd
M3 - Conference contribution
AN - SCOPUS:85202009332
SN - 9789811851841
T3 - Proceedings of the 8th International Symposium on Reliability Engineering and Risk Management, ISRERM 2022
SP - 160
EP - 165
BT - Proceedings of the 8th International Symposium on Reliability Engineering and Risk Management, ISRERM 2022
A2 - Beer, Michael
A2 - Zio, Enrico
A2 - Phoon, Kok-Kwang
A2 - Ayyub, Bilal M.
T2 - 8th International Symposium on Reliability Engineering and Risk Management, ISRERM 2022
Y2 - 4 September 2022 through 7 September 2022
ER -