TY - JOUR
T1 - From inference to design: A comprehensive framework for uncertainty quantification in engineering with limited information
AU - Gray, Ander
AU - Wimbush, Alexander
AU - de Angelis, Marco
AU - Hristov, Peter O.
AU - Calleja, Dominic
AU - Miralles-Dolz, Enrique
AU - Rocchetta, Roberto
PY - 2022/2/15
Y1 - 2022/2/15
N2 - 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.
AB - 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.
KW - Bayesian calibration
KW - Probability bounds analysis
KW - Epistemic uncertainty
KW - Uncertainty propagation
KW - Uncertainty reduction
KW - Optimisation under uncertainty
U2 - 10.1016/j.ymssp.2021.108210
DO - 10.1016/j.ymssp.2021.108210
M3 - Article
SN - 0888-3270
VL - 165
JO - Mechanical Systems and Signal Processing
JF - Mechanical Systems and Signal Processing
M1 - 108210
ER -