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Title: Local polynomial chaos expansion for linear differential equations with high dimensional random inputs

In this paper we present a localized polynomial chaos expansion for partial differential equations (PDE) with random inputs. In particular, we focus on time independent linear stochastic problems with high dimensional random inputs, where the traditional polynomial chaos methods, and most of the existing methods, incur prohibitively high simulation cost. Furthermore, the local polynomial chaos method employs a domain decomposition technique to approximate the stochastic solution locally. In each subdomain, a subdomain problem is solved independently and, more importantly, in a much lower dimensional random space. In a postprocesing stage, accurate samples of the original stochastic problems are obtained from the samples of the local solutions by enforcing the correct stochastic structure of the random inputs and the coupling conditions at the interfaces of the subdomains. Overall, the method is able to solve stochastic PDEs in very large dimensions by solving a collection of low dimensional local problems and can be highly efficient. In our paper we present the general mathematical framework of the methodology and use numerical examples to demonstrate the properties of the method.
 [1] ;  [2] ;  [3] ;  [4]
  1. Purdue Univ., West Lafayette, IN (United States)
  2. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
  3. ETH Zurich, Zurich (Switzerland)
  4. Univ. of Utah, Salt Lake City, UT (United States)
Publication Date:
OSTI Identifier:
Report Number(s):
Journal ID: ISSN 1064-8275; 567323
DOE Contract Number:
Resource Type:
Journal Article
Resource Relation:
Journal Name: SIAM Journal on Scientific Computing; Journal Volume: 37; Journal Issue: 1
Research Org:
Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org:
USDOE National Nuclear Security Administration (NNSA)
Country of Publication:
United States
97 MATHEMATICS AND COMPUTING generalized ploynomial chaos; domain decomposition; stochastic differential equation; uncertainty quantification