Lye, Adolphus, Gray, Ander 
ORCID: 0000-0002-1585-0900, de Angelis, Marco ORCID: 0000-0001-8851-023X and Ferson, Scott ORCID: 0000-0002-2613-0650
(2023)
ROBUST PROBABILITY BOUNDS ANALYSIS FOR FAILURE ANALYSIS UNDER LACK OF DATA AND MODEL UNCERTAINTY.
In: 5th International Conference on Uncertainty Quantification in Computational Sciences and Engineering, 2023-6-12 - 2023-6-14, Athens, Greece.
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Abstract
The paper serves as a response to the recent challenge problem published by the NAFEMS Stochastic Working Group titled: “Uncertain Knowledge: A Challenge Problem” whereby the participants are to implement current practices and ‘state-of-the-art’ stochastic methods to address numerous uncertainty quantification problems presented in the challenge. In total, two different challenge problems on increasing complexity levels are addressed through the use of the following techniques: 1) Bayesian model updating for the calibration of the distribution models and model selection for the aleatory variables of interest; 2) Adaptive-pinching method for the sensitivity analysis; and 3) Probability Bounds Analysis to quantify the uncertainty over the failure probabilities. For the reproducibility of the results and to provide a better understanding of the numerical techniques discussed in the paper, the MATLAB and R codes implemented to address the challenge problems are made available via: https://github.com/Institute-for-Risk-and-Uncer NAFEMS-UQ-Challenge-2022
| Item Type: | Conference or Workshop Item (Unspecified) |
|---|---|
| Uncontrolled Keywords: | Interval arithmetic, Probability box, Bayesian inference, Transitional Ensemble Markov Chain Monte Carlo, Adaptive pinching, Model uncertainty, Dependence |
| Divisions: | Faculty of Science and Engineering > School of Engineering |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 06 Mar 2023 10:28 |
| Last Modified: | 06 Dec 2024 14:06 |
| DOI: | 10.7712/120223.10345.19797 |
| Related URLs: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3168766 |
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