Solving elliptic boundary value problems with uncertain coefficients by the finite element method: The stochastic formulation

Ivo Babuŝka*, Raúl Tempone, Georgios E. Zouraris

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

256 Scopus citations


This work studies a linear elliptic problem with uncertainty. The introduction gives a survey of different formulations of the uncertainty and resulting numerical approximations. The major emphasis of this work is the probabilistic treatment of uncertainty, addressing the problem of solving linear elliptic boundary value problems with stochastic coefficients. If the stochastic coefficients are known functions of a random vector, then the stochastic elliptic boundary value problem is turned into a parametric deterministic one with solution u(y, x), y ε Γ, x ε D, where D ⊂ ℝd, d = 1, 2, 3, and Γ is a high-dimensional cube. In addition, the function u is specified as the solution of a deterministic variational problem over Γ × D. A tensor product finite element method, of h-version in D and k-, or, p-version in Γ, is proposed for the approximation of u. A priori error estimates are given and an adaptive algorithm is also proposed. Due to the high dimension of Γ, the Monte Carlo finite element method is also studied here. This work compares the asymptotic complexity of the numerical methods, and shows results from numerical experiments. Comments on the uncertainty in the probabilistic characterization of the coefficients in the stochastic formulation are included.

Original languageEnglish (US)
Pages (from-to)1251-1294
Number of pages44
JournalComputer Methods in Applied Mechanics and Engineering
Issue number12-16
StatePublished - Apr 8 2005
Externally publishedYes


  • Adaptive methods
  • Error control
  • Error estimates
  • Expected value
  • Finite elements
  • Karhunen-Loève expansion
  • Monte Carlo methods
  • Perturbation estimstes
  • Stochastic elliptic equation
  • k × h-vesions
  • p × h-versions

ASJC Scopus subject areas

  • Computational Mechanics
  • Mechanics of Materials
  • Mechanical Engineering
  • General Physics and Astronomy
  • Computer Science Applications


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