Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6917722 | Computer Methods in Applied Mechanics and Engineering | 2014 | 22 Pages |
Abstract
This work is concerned with the propagation of uncertainty across coupled domain problems with high-dimensional random inputs. A stochastic model reduction approach based on low-rank separated representations is proposed for the partitioned treatment of the uncertainty space. The construction of the coupled domain solution is achieved though a sequence of approximations with respect to the dimensionality of the random inputs associated with each individual sub-domain and not the combined dimensionality, hence drastically reducing the overall computational cost. The coupling between the sub-domain solutions is done via the classical finite element tearing and interconnecting (FETI) method, thus providing a well suited framework for parallel computing. Two high-dimensional stochastic problems, a 2D elliptic PDE with random diffusion coefficient and a stochastic linear elasticity problem, have been considered to study the performance and accuracy of the proposed stochastic coupling approach.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Science Applications
Authors
Mohammad Hadigol, Alireza Doostan, Hermann G. Matthies, Rainer Niekamp,