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

A. Gray, A. Wimbush, M. de Angelis, P.O. Hristov, D. Calleja, E. Miralles-Dolz, R. Rocchetta

Research output: Contribution to journalArticlepeer-review

35 Citations (Scopus)
33 Downloads (Pure)

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.
Original languageEnglish
Article number108210
Number of pages39
JournalMechanical Systems and Signal Processing
Volume165
Early online date25 Aug 2021
DOIs
Publication statusPublished - 15 Feb 2022

Keywords

  • Bayesian calibration
  • probability bounds analysis
  • uncertainty propagation
  • uncertainty reduction
  • epistemic uncertainty
  • optimisation under uncertainty

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