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Method of Distributions for Uncertainty Quantification

In: Handbook of Uncertainty Quantification

Author

Listed:
  • Daniel M. Tartakovsky

    (University of California, San Diego, Department of Mechanical and Aerospace Engineering)

  • Pierre A. Gremaud

    (North Carolina State University, Department of Mathematics)

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.

Suggested Citation

  • Daniel M. Tartakovsky & Pierre A. Gremaud, 2017. "Method of Distributions for Uncertainty Quantification," Springer Books, in: Roger Ghanem & David Higdon & Houman Owhadi (ed.), Handbook of Uncertainty Quantification, chapter 22, pages 763-783, Springer.
    • Handle: RePEc:spr:sprchp:978-3-319-12385-1_27
    DOI: 10.1007/978-3-319-12385-1_27
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