Method of Distributions for Uncertainty Quantification

Daniel M. Tartakovsky () and Pierre A. Gremaud ()
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Daniel M. Tartakovsky: University of California, San Diego, Department of Mechanical and Aerospace Engineering
Pierre A. Gremaud: North Carolina State University, Department of Mathematics

Chapter 22 in Handbook of Uncertainty Quantification, 2017, pp 763-783 from Springer

Abstract: Abstract Parametric uncertainty, considered broadly to include uncertainty in system parameters and driving forces (source terms and initial and boundary conditions), is ubiquitous in mathematical modeling. The method of distributions, which comprises PDF and CDF methods, quantifies parametric uncertainty by deriving deterministic equations for either probability density function (PDF) or cumulative distribution function (CDF) of model outputs. Since it does not rely on finite-term approximations (e.g., a truncated Karhunen-Loève transformation) of random parameter fields, the method of distributions does not suffer from the “curse of dimensionality.” On the contrary, it is exact for a class of nonlinear hyperbolic equations whose coefficients lack spatiotemporal correlation, i.e., exhibit an infinite number of random dimensions.

Keywords: Random; Stochastic; Probability density function (PDF); Cumulative distribution function (CDF); Langevin equation; White noise; Colored noise; Fokker-Planck equation (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1007/978-3-319-12385-1_27

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