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K. Saberfield, “Monte Carlo Methods for Stochastic PDEs,” Institute for Applied Analysis and Stochastic, Berlin, 2007.

has been cited by the following article:

• TITLE: A View on Stochastic Finite Element and Geostatistics for Resource Parameters Estimation

  AUTHORS: Skender Osmani, Mihallaq Kotro, Ervin Toromani, Aida Bode, Arben Boçari

  KEYWORDS: Parameter Estimation; SFE; Geostatistics; Kriging; Risk Analysis; Optimisation

  JOURNAL NAME: Open Journal of Modern Hydrology, Vol.3 No.2, April 29, 2013

  ABSTRACT: The resource parameter estimation using stochastic finite element, geostatistics etc. is a key point on uncertainty, risk analysis, optimization [1-5] etc. In this view, the paper presents some consideration on: 1) Stochastic finite element estimation. The concept of random element is simplified as a stochastic finite element (SFE) taking into account a parallelepiped element with eight nodes in which are given the probability density functions (pdf) on its point supports. In this context it is shown: a—the stochastic finite element is a linear interpolator, related to the distributions given at each nodes; b—the distribution pdf in whatever point x ∈ V; c—the estimation of the mean value of Z(x); 2) Volume integrals calculus; 3) SFE in geostatistics approaches; 4) SFE in PDE solution. Finally, some conclusions are presented underlying the importance of SFE applications