Library Subscription: Guest
Begell Digital Portal Begell Digital Library eBooks Journals References & Proceedings Research Collections
International Journal for Uncertainty Quantification
IF: 3.259 5-Year IF: 2.547 SJR: 0.417 SNIP: 0.8 CiteScore™: 1.52

ISSN Print: 2152-5080
ISSN Online: 2152-5099

Open Access

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

ABSTRACT

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.


Articles with similar content:

DISCRETE-CONTINUAL VARIATION-DIFFERENCE METHOD OF ANALYSIS FOR TWO-DIMENSIONAL AND THREE-DIMENSIONAL PROBLEMS OF STRUCTURAL ANALYSIS
International Journal for Computational Civil and Structural Engineering, Vol.1, 2005, issue 2
Pavel A. Akimov, Alexander B. Zolotov
A DAMAGE PARTICLE METHOD FOR SMEARED MODELING OF BRITTLE FRACTURE
International Journal for Multiscale Computational Engineering, Vol.16, 2018, issue 4
Jiun-Shyan Chen, Haoyan Wei
Stabilization of Families of Linear and Nonlinear Discrete Dynamical Systems
Journal of Automation and Information Sciences, Vol.49, 2017, issue 1
Vsevolod M. Kuntsevich
HIERARCHICAL SPARSE BAYESIAN LEARNING FOR STRUCUTRAL HEALTH MONITORING WITH INCOMPLETE MODAL DATA
International Journal for Uncertainty Quantification, Vol.5, 2015, issue 2
Yong Huang, James L. Beck
NONLOCAL GRADIENT-DEPENDENT CONSTITUTIVE MODEL FOR SIMULATING LOCALIZED DAMAGE AND FRACTURE OF VISCOPLASTIC SOLIDS UNDER HIGH-ENERGY IMPACTS
International Journal for Multiscale Computational Engineering, Vol.10, 2012, issue 5
Anthony N. Palazotto, Rashid K. Abu Al-Rub