Non-intrusive Methods

  • O. P. Le MaîtreEmail author
  • O. M. Knio
Part of the Scientific Computation book series (SCIENTCOMP)


In this chapter, we focus our attention on non-intrusive methods for the approximation of an output of a model involving random data, parametrized by a finite set of independent random parameters defined on a suitable probability space. As discussed Chap. 2, we are concerned with models having a unique solution for almost all realizations of the random parameters, so the model can be seen as a surjective mapping from the parameters domain to the image solution space. Because this mapping involves models which are generally complex to solve (for instance PDEs), a natural idea that has been used for a long time is to construct a much simpler mapping, or surrogate model, that approximates the actual complex model. To this end, the so-called non-intrusive methods rely on a set of deterministic model resolutions, corresponding to some specific realizations, to construct the surrogate model. Along this line, a deterministic simulation code can be used as a black-box, which associates to each realization of the parameters the corresponding model output.


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Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  1. 1.LIMSI-CNRSUniversité Paris-Sud XIOrsay cedexFrance
  2. 2.Department of Mechanical EngineeringThe Johns Hopkins UniversityBaltimoreUSA

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