Abstract
In this work we consider quasi-optimal versions of the Stochastic Galerkin method for solving linear elliptic PDEs with stochastic coefficients. In particular, we consider the case of a finite number N of random inputs and an analytic dependence of the solution of the PDE with respect to the parameters in a polydisc of the complex plane CN. We show that a quasi-optimal approximation is given by a Galerkin projection on a weighted (anisotropic) total degree space and prove a (sub)exponential convergence rate. As a specific application we consider a thermal conduction problem with non-overlapping inclusions of random conductivity. Numerical results show the sharpness of our estimates.
| Original language | English |
|---|---|
| Pages (from-to) | 732-751 |
| Number of pages | 20 |
| Journal | Computers and Mathematics with Applications |
| Volume | 67 |
| Issue number | 4 |
| DOIs | |
| State | Published - Mar 2014 |
| Externally published | Yes |
Keywords
- Best M-terms polynomial approximation
- Elliptic PDEs with random data
- Multivariate polynomial approximation
- Stochastic Galerkin method
- Subexponential convergence
- Uncertainty quantification
ASJC Scopus subject areas
- Modeling and Simulation
- Computational Theory and Mathematics
- Computational Mathematics