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Title: Basis adaptation and domain decomposition for steady partial differential equations with random coefficients

In this paper, we present a novel approach for solving steady-state stochastic partial differential equations (PDEs) with high-dimensional random parameter space. The proposed approach combines spatial domain decomposition with basis adaptation for each subdomain. The basis adaptation is used to address the curse of dimensionality by constructing an accurate low-dimensional representation of the stochastic PDE solution (probability density function and/or its leading statistical moments) in each subdomain. Restricting the basis adaptation to a specific subdomain affords finding a locally accurate solution. Then, the solutions from all of the subdomains are stitched together to provide a global solution. We support our construction with numerical experiments for a steady-state diffusion equation with a random spatially dependent coefficient. Lastly, our results show that highly accurate global solutions can be obtained with significantly reduced computational costs.
Authors:
 [1] ;  [1] ;  [1]
  1. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Publication Date:
Report Number(s):
PNNL-SA-115134
Journal ID: ISSN 0021-9991; PII: S0021999117306484
Grant/Contract Number:
AC05-76RL01830
Type:
Accepted Manuscript
Journal Name:
Journal of Computational Physics
Additional Journal Information:
Journal Volume: 351; Journal ID: ISSN 0021-9991
Publisher:
Elsevier
Research Org:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org:
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21)
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; basis adaptation; dimension reduction; domain decomposition; polynomial chaos; uncertainty quantification
OSTI Identifier:
1379948
Alternate Identifier(s):
OSTI ID: 1395272

Tipireddy, R., Stinis, P., and Tartakovsky, A. M.. Basis adaptation and domain decomposition for steady partial differential equations with random coefficients. United States: N. p., Web. doi:10.1016/j.jcp.2017.08.067.
Tipireddy, R., Stinis, P., & Tartakovsky, A. M.. Basis adaptation and domain decomposition for steady partial differential equations with random coefficients. United States. doi:10.1016/j.jcp.2017.08.067.
Tipireddy, R., Stinis, P., and Tartakovsky, A. M.. 2017. "Basis adaptation and domain decomposition for steady partial differential equations with random coefficients". United States. doi:10.1016/j.jcp.2017.08.067. https://www.osti.gov/servlets/purl/1379948.
@article{osti_1379948,
title = {Basis adaptation and domain decomposition for steady partial differential equations with random coefficients},
author = {Tipireddy, R. and Stinis, P. and Tartakovsky, A. M.},
abstractNote = {In this paper, we present a novel approach for solving steady-state stochastic partial differential equations (PDEs) with high-dimensional random parameter space. The proposed approach combines spatial domain decomposition with basis adaptation for each subdomain. The basis adaptation is used to address the curse of dimensionality by constructing an accurate low-dimensional representation of the stochastic PDE solution (probability density function and/or its leading statistical moments) in each subdomain. Restricting the basis adaptation to a specific subdomain affords finding a locally accurate solution. Then, the solutions from all of the subdomains are stitched together to provide a global solution. We support our construction with numerical experiments for a steady-state diffusion equation with a random spatially dependent coefficient. Lastly, our results show that highly accurate global solutions can be obtained with significantly reduced computational costs.},
doi = {10.1016/j.jcp.2017.08.067},
journal = {Journal of Computational Physics},
number = ,
volume = 351,
place = {United States},
year = {2017},
month = {9}
}