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International Journal for Uncertainty Quantification
Facteur d'impact: 4.911 Facteur d'impact sur 5 ans: 3.179 SJR: 1.008 SNIP: 0.983 CiteScore™: 5.2

ISSN Imprimer: 2152-5080
ISSN En ligne: 2152-5099

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International Journal for Uncertainty Quantification

DOI: 10.1615/Int.J.UncertaintyQuantification.2012004275
pages 421-444

PROPAGATION OF UNCERTAINTY BY SAMPLING ON CONFIDENCE BOUNDARIES

Jan Peter Hessling
SP Technical Research Institute of Sweden, Measurement Technology, Box 857, SE-50115 Boras, Sweden
Thomas Svensson
SP Technical Research Institute of Sweden, Building Technology and Mechanics, Box 857, SE-50115 Boras, Sweden

RÉSUMÉ

A new class of methods for propagation of uncertainty through complex models nonlinear-in-parameters is proposed. It is derived from a recent idea of propagating covariance within the unscented Kalman filter. The nonlinearity could be due to a pole-zero parametrization of a dynamic model in the Laplace domain, finite element model (FEM) or other large computer models, models of mechanical fatigue etc. Two approximate methods of this class are evaluated against Monte Carlo simulations and compared to the application of the Gauss approximation formula. Three elementary static models illustrate pros and cons of the methods, while one dynamic model provides a realistic simple example of its use.


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