Method of Distributions for Uncertainty Quantification

  • Daniel M. TartakovskyEmail author
  • Pierre A. Gremaud
Reference work entry


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.


Random Stochastic Probability density function (PDF) Cumulative distribution function (CDF) Langevin equation White noise Colored noise Fokker-Planck equation 


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Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  1. 1.Department of Mechanical and Aerospace EngineeringUniversity of California, San DiegoLa JollaUSA
  2. 2.Department of MathematicsNorth Carolina State UniversityRaleighUSA

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