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On information fusion for reliability estimation with multifidelity models

Proppe, Carsten 1; Kaupp, Jonas 1
1 Institut für Technische Mechanik (ITM), Karlsruher Institut für Technologie (KIT)

Abstract:

Multifidelity models attempt to reduce the computational effort by combining simulation models of different approximation quality and from different sources. Information fusion combines outputs from a model hierarchy in order to obtain efficient estimators for a quantity of interest. In this paper, information fusion is applied to reliability estimation. To this end, efficient multifidelity estimators for the probability of failure are developed by combining additive and multiplicative information fusion with importance sampling and importance splitting (notably the moving particles method). Importance sampling and importance splitting based multifidelity reliability estimators are compared focusing on relative error and coefficient of variation.


Verlagsausgabe §
DOI: 10.5445/IR/1000147985
Veröffentlicht am 22.06.2022
Originalveröffentlichung
DOI: 10.1016/j.probengmech.2022.103291
Scopus
Zitationen: 4
Web of Science
Zitationen: 1
Dimensions
Zitationen: 4
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Technische Mechanik (ITM)
Publikationstyp Zeitschriftenaufsatz
Publikationsmonat/-jahr 07.2022
Sprache Englisch
Identifikator ISSN: 0266-8920, 1878-4275
KITopen-ID: 1000147985
Erschienen in Probabilistic Engineering Mechanics
Verlag Elsevier
Band 69
Seiten Art.-Nr.: 103291
Schlagwörter Multifidelity; Model hierarchy; Information fusion; Reliability estimation; Moving particles algorithm; Importance sampling
Nachgewiesen in Scopus
Web of Science
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