Black-box propagation of failure probabilities under epistemic uncertainty

Marco De Angelis, Scott Ferson, Edoardo Patelli, Vladik Kreinovich

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

Abstract

In engineering simulation a black-box code is often a complex, legacy or proprietary (secret) black-box software used to describe the physics of the system under study. Strategies to propagate epistemic uncertainty through such codes are desperately needed, for code verification, sensitivity, and validation on experimental data. Very often in practice, the uncertainty in the inputs is characterised by imprecise probability distributions or distributions with interval parameters, also known as probability boxes. In this paper we propose a strategy based on line sampling to propagate both aleatory and epistemic uncertainty through black-box codes to obtain interval probabilities of failure. The efficiency of the proposed strategy is demonstrated on the NASA LaRC UQ problem.

Original languageEnglish
Title of host publicationProceedings of the 3rd International Conference on Uncertainty Quantification in Computational Sciences and Engineering, UNCECOMP 2019
EditorsM. Papadrakakis, V. Papadopoulos, G. Stefanou
Place of PublicationAthens
Pages713-723
Number of pages11
DOIs
Publication statusPublished - 24 Jun 2019
Event3rd International Conference on Uncertainty Quantification in Computational Sciences and Engineering - Crete, Greece
Duration: 24 Jun 201926 Jun 2019

Conference

Conference3rd International Conference on Uncertainty Quantification in Computational Sciences and Engineering
Abbreviated titleUNCECOMP 2019
CountryGreece
CityCrete
Period24/06/1926/06/19

Keywords

  • black-box code
  • Cauchy-deviate method
  • digital twins
  • line sampling

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  • Cite this

    De Angelis, M., Ferson, S., Patelli, E., & Kreinovich, V. (2019). Black-box propagation of failure probabilities under epistemic uncertainty. In M. Papadrakakis, V. Papadopoulos, & G. Stefanou (Eds.), Proceedings of the 3rd International Conference on Uncertainty Quantification in Computational Sciences and Engineering, UNCECOMP 2019 (pp. 713-723). https://doi.org/10.7712/120219.6373.18699