Robust optimization of a dynamic Black-box system under severe uncertainty: A distribution-free framework

Lye, Adolphus ORCID: 0000-0002-1803-8344, Kitahara, Masaru, Broggi, Matteo and Patelli, Edoardo ORCID: 0000-0002-5007-7247 (2022) Robust optimization of a dynamic Black-box system under severe uncertainty: A distribution-free framework. MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 167. p. 108522.

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Official URL: http://dx.doi.org/10.1016/j.ymssp.2021.108522

Abstract

In the real world, a significant challenge faced in designing critical systems is the lack of available data. This results in a large degree of uncertainty and the need for uncertainty quantification tools so as to make risk-informed decisions. The NASA-Langley UQ Challenge 2019 seeks to provide such setting, requiring different discipline-independent approaches to address typical tasks required for the design of critical systems. This paper addresses the NASA-Langley UQ Challenge by proposing 4 key techniques to provide the solution to the challenge: (1) a distribution-free Bayesian model updating framework for the calibration of the uncertainty model; (2) an adaptive pinching approach to analyse and rank the relative sensitivity of the epistemic parameters; (3) the probability bounds analysis to estimate failure probabilities; and (4) a Non-intrusive Stochastic Simulation approach to identify an optimal design point.

Item Type:	Article
Uncontrolled Keywords:	Uncertainty quantification, Model class selection, Sensitivity analysis, Staircase density function, Robust optimization, Non-intrusive imprecise stochastic simulation
Divisions:	Faculty of Science and Engineering > School of Engineering
Depositing User:	Symplectic Admin
Date Deposited:	07 Oct 2021 15:29
Last Modified:	18 Jan 2023 21:27
DOI:	10.1016/j.ymssp.2021.108522
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3139616