Zhang, Zhiwen and Hu, Xin and Hou, Thomas Y. and Lin, Guang and Yan, Mike
(2014)
*Adaptive ANOVA-Based Data-Driven Stochastic Method for Elliptic PDEs with Random Coefficient.*
Communications in Computational Physics, 16
(3).
pp. 571-598.
ISSN 1815-2406.
doi:10.4208/cicp.270913.020414a.
https://resolver.caltech.edu/CaltechAUTHORS:20150112-104708436

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## Abstract

In this paper, we present an adaptive, analysis of variance (ANOVA)-based data-driven stochastic method (ANOVA-DSM) to study the stochastic partial differential equations (SPDEs) in the multi-query setting. Our new method integrates the advantages of both the adaptive ANOVA decomposition technique and the data-driven stochastic method. To handle high-dimensional stochastic problems, we investigate the use of adaptive ANOVA decomposition in the stochastic space as an effective dimension-reduction technique. To improve the slow convergence of the generalized polynomial chaos (gPC) method or stochastic collocation (SC) method, we adopt the data-driven stochastic method (DSM) for speed up. An essential ingredient of the DSM is to construct a set of stochastic basis under which the stochastic solutions enjoy a compact representation for a broad range of forcing functions and/or boundary conditions. Our ANOVA-DSM consists of offline and online stages. In the offline stage, the original high-dimensional stochastic problem is decomposed into a series of low-dimensional stochastic subproblems, according to the ANOVA decomposition technique. Then, for each subproblem, a data-driven stochastic basis is computed using the Karhunen-Loeve expansion (KLE) and a two-level preconditioning optimization approach. Multiple trial functions are used to enrich the stochastic basis and improve the accuracy. In the online stage, we solve each stochastic subproblem for any given forcing function by projecting the stochastic solution into the data-driven stochastic basis constructed offline. In our ANOVA-DSM framework, solving the original high-dimensional stochastic problem is reduced to solving a series of ANOVA-decomposed stochastic subproblems using the DSM. An adaptive ANOVA strategy is also provided to further reduce the number of the stochastic subproblems and speed up our method. To demonstrate the accuracy and efficiency of our method, numerical examples are presented for one- and two-dimensional elliptic PDEs with random coefficients.

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Additional Information: | © 2014 Global-Science Press. Received 27 September 2013; accepted (in revised version) 2 April 2014; available online 24 June 2014. The work of Thomas Hou was supported in part by an AFOSR MURI project under Contract FA 9550-09-1-0613, a DOE Grant DE-FG02-06ER25727, and NSF FRG Grant DMS-1159138. The work of Guang Lin was supported by the U.S. Department of Energy (DOE) Office of Science Advanced Scientific Computing Research Applied Mathematics program. Pacific Northwest National Laboratory is operated by Battelle for the DOE under Contract DE-AC05-76RL01830. | ||||||||||

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Subject Keywords: | Analysis of variance; stochastic partial differential equations; data-driven methods; Karhunen-Loeve expansion; uncertainty quantification; model reduction | ||||||||||

Issue or Number: | 3 | ||||||||||

DOI: | 10.4208/cicp.270913.020414a | ||||||||||

Record Number: | CaltechAUTHORS:20150112-104708436 | ||||||||||

Persistent URL: | https://resolver.caltech.edu/CaltechAUTHORS:20150112-104708436 | ||||||||||

Usage Policy: | No commercial reproduction, distribution, display or performance rights in this work are provided. | ||||||||||

ID Code: | 53563 | ||||||||||

Collection: | CaltechAUTHORS | ||||||||||

Deposited By: | Jason Perez | ||||||||||

Deposited On: | 13 Jan 2015 02:16 | ||||||||||

Last Modified: | 10 Nov 2021 20:04 |

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