From inference to design: A comprehensive framework for uncertainty quantification in engineering with limited information
| Autor: | Enrique Miralles-Dolz, M. de Angelis, P.O. Hristov, Dominic Calleja, Ander Gray, Alexander Wimbush, Roberto Rocchetta |
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| Přispěvatelé: | Industrial Statistics, Eindhoven MedTech Innovation Center, Security, EAISI Health, EAISI High Tech Systems |
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
Propagation of uncertainty
Probability bounds analysis Epistemic uncertainty Computer science Mechanical Engineering Bayesian calibration Aerospace Engineering Inference Computer Science Applications Variety (cybernetics) Control and Systems Engineering Optimisation under uncertainty Signal Processing Uncertainty propagation Systems engineering Uncertainty quantification Engineering design process Uncertainty reduction Reliability (statistics) Uncertainty reduction theory Civil and Structural Engineering |
| Zdroj: | Mechanical Systems and Signal Processing, 165:108210. Academic Press Inc. Mechanical Systems and Signal Processing |
| ISSN: | 0888-3270 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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