TY - JOUR

T1 - Robust optimization of a dynamic Black-box system under severe uncertainty

T2 - A distribution-free framework

AU - Lye, Adolphus

AU - Kitahara, Masaru

AU - Broggi, Matteo

AU - Patelli, Edoardo

N1 - Funding Information: This work has been partially funded by the Deutsche Forschungsgemeinsschaft (DFG, German Research Foundation) — SFB1463-434502799 .

PY - 2022/3/15

Y1 - 2022/3/15

N2 - 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.

AB - 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.

KW - Model class selection

KW - Non-intrusive imprecise stochastic simulation

KW - Robust optimization

KW - Sensitivity analysis

KW - Staircase density function

KW - Uncertainty quantification

UR - http://www.scopus.com/inward/record.url?scp=85118231987&partnerID=8YFLogxK

U2 - 10.1016/j.ymssp.2021.108522

DO - 10.1016/j.ymssp.2021.108522

M3 - Article

AN - SCOPUS:85118231987

VL - 167

JO - Mechanical Systems and Signal Processing

JF - Mechanical Systems and Signal Processing

SN - 0888-3270

IS - Part A

M1 - 108522

ER -