In general, the behavior of science and engineering is predicted based on nonlinear math models. Imprecise knowledge of the model parameters alters the system response from the assumed nominal model data. One proposes an algorithm for generating insights into the range of variability that can be expected due to model uncertainty. An automatic differentiation tool builds the exact partial derivative models required to develop a state transition tensor series (STTS)-based solution for nonlinearly mapping initial uncertainty models into instantaneous uncertainty models. The fully nonlinear statistical system properties are recovered via series approximations. The governing nonlinear probability distribution function is approximated by developing an inverse mapping algorithm for the forward series model. Numerical examples are presented, which demonstrate the effectiveness of the proposed methodology.
Semi-Analytic Probability Density Function for System Uncertainty
Manuscript received January 28, 2016; final manuscript received June 13, 2016; published online August 19, 2016. Assoc. Editor: Athanasios Pantelous.
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Younes, A. B., and Turner, J. (August 19, 2016). "Semi-Analytic Probability Density Function for System Uncertainty." ASME. ASME J. Risk Uncertainty Part B. December 2016; 2(4): 041007. https://doi.org/10.1115/1.4033886
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