From inference to design: A comprehensive framework for uncertainty quantification in engineering with limited information

Gray, A ORCID: 0000-0002-1585-0900, Wimbush, A, de Angelis, M ORCID: 0000-0001-8851-023X, Hristov, PO ORCID: 0000-0002-3302-686X, Calleja, D, Miralles-Dolz, E and Rocchetta, R (2022) From inference to design: A comprehensive framework for uncertainty quantification in engineering with limited information. Mechanical Systems and Signal Processing, 165. p. 108210.

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Official URL: http://dx.doi.org/10.1016/j.ymssp.2021.108210

Abstract

In this paper we present a framework for addressing a variety of engineering design challenges with limited empirical data and partial information. This framework includes guidance on the characterisation of a mixture of uncertainties, efficient methodologies to integrate data into design decisions, and to conduct reliability analysis, and risk/reliability based design optimisation. To demonstrate its efficacy, the framework has been applied to the NASA 2020 uncertainty quantification challenge. The results and discussion in the paper are with respect to this application.

Item Type:	Article
Uncontrolled Keywords:	Bayesian calibration, Probability bounds analysis, Uncertainty propagation, Uncertainty reduction, Epistemic uncertainty, Optimisation under uncertainty
Divisions:	Faculty of Science and Engineering > School of Engineering
Depositing User:	Symplectic Admin
Date Deposited:	26 Aug 2021 07:42
Last Modified:	18 Jan 2023 21:32
DOI:	10.1016/j.ymssp.2021.108210
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3134719