an:05291142
Zbl 1151.65008
Babuška, Ivo; Nobile, Fabio; Tempone, Raúl
A stochastic collocation method for elliptic partial differential equations with random input data
EN
SIAM J. Numer. Anal. 45, No. 3, 1005-1034 (2007).
0036-1429 1095-7170
2007
j
65C30 65N35 65N15 65N12 65N30 60H15 35R60 60H35
stochastic partial differential equations; finite elements; uncertainty quantification; exponential convergence; error bounds; stochastic collocation method; stochastic Galerkin method; numerical examples
The authors propose and analyze a stochastic collocation method to solve elliptic partial differential equations with random coefficients and forcing terms (input data of the model). The input data are assumed to depend on a finite number of random variables. The method consists in a Galerkin approximation in space and a collocation in the zeros of suitable tensor product orthogonal polynomials (Gauss points) in the probability space and naturally leads to the solution of uncoupled deterministic problems as in the Monte Carlo approach. It can be seen as a generalization of the stochastic Galerkin method proposed by \textit{I. Babuška, R. Tempone}, and \textit{G. E. Zouraris} [SIAM J. Numer. Anal. 42, No. 2, 800--825 (2004; Zbl 1080.65003)].
It allows to treat easily a wider range of situations, such as input data that depend non linearly on the random variables, diffusivity coefficients with unbounded second moments, and random variables that are correlated or even unbounded. A rigorous convergence analysis is provided and exponential convergence of the probability error with respect to the number of Gauss points in each direction in the probability space is proved under some regularity assumptions on the random input data. Numerical examples show the effectiveness of the method.
Dominique Lepingle (Orléans)
1080.65003