Abstract
Uncertainty quantification is a vital part of all engineering and scientific pursuits. Some of the current most challenging tasks in UQ involve accurately calibrating, propagating and performing optimisation under aleatory and epistemic uncertainty in high dimensional models with very few data; like the challenge proposed by Nasa Langley this year. In this paper we propose a solution which clearly separates aleatory from epistemic uncertainty. A multidimensional 2nd-order distribution was calibrated with Bayesian updating and used as an inner approximation to a p-box. A sliced normal distribution was fit to the posterior, and used to produce cheap samples while keeping the posterior dependence structure. The remaining tasks, such as sensitivity and reliability optimisation, are completed with probability bounds analysis. These tasks were repeated a number of times as designs were improved and more data gathered.
Original language | English |
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Title of host publication | e-proceedings of the 30th European Safety and Reliability Conference and 15th Probabilistic Safety Assessment and Management Conference |
Editors | Piero Baraldi, Francesco Di Maio, Enrico Zio |
Place of Publication | Singapore |
Number of pages | 8 |
ISBN (Electronic) | 9789811485930 |
DOIs | |
Publication status | Published - 5 Nov 2020 |
Event | The 30th European Safety and Reliability Conference and The 15th Probabilistic Safety Assessment and Management Conference - Online, Venice, Italy Duration: 1 Nov 2020 → 5 Nov 2020 https://www.esrel2020-psam15.org/ |
Conference
Conference | The 30th European Safety and Reliability Conference and The 15th Probabilistic Safety Assessment and Management Conference |
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Abbreviated title | ESREL 2020 PSAM 15 |
Country/Territory | Italy |
City | Venice |
Period | 1/11/20 → 5/11/20 |
Internet address |
Keywords
- Bayesian calibration
- 2nd-order distribution
- probability bounds analysis
- uncertainty propagation
- uncertainty reduction
- epistemic uncertainty
- optimization under uncertainty