PROPAGATION OF UNCERTAINTY BY SAMPLING ON CONFIDENCE BOUNDARIES

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.

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