Efficient estimation of space varying parameters in numerical models using non-intrusive subdomain reduced order modeling

•A subdomain reduced-order linear model is constructed for an easy implementation of adjoint.•A smooth local parameterization is proposed to represent spatial parameters independently in each subdomain.•The CPU cost is reduced by a factor of several orders of the magnitude while reasonable accuracy...

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Bibliographic Details
Published in:Journal of computational physics 2021-01, Vol.424, p.109867, Article 109867
Main Authors: Xiao, Cong, Leeuwenburgh, Olwijn, Lin, Hai Xiang, Heemink, Arnold
Format: Article
Language:English
Subjects:
Adjoint model
Algorithms
Computational physics
Computing costs
Domain decomposition
Domain decomposition methods
Mathematical models
Model reduction
Numerical models
Parameter estimation
Parameter uncertainty
Parameterization
Reduced order models
Smoothness
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Summary:•A subdomain reduced-order linear model is constructed for an easy implementation of adjoint.•A smooth local parameterization is proposed to represent spatial parameters independently in each subdomain.•The CPU cost is reduced by a factor of several orders of the magnitude while reasonable accuracy remains. A reduced order modeling algorithm for the estimation of space varying parameter patterns in numerical models is proposed. In this approach domain decomposition is applied to construct separate approximations to the numerical model in every subdomain. We introduce a new local parameterization that decouples the computational cost of the algorithm from the number of global principal components and therefore provides attractive scaling for models with a very large number of uncertain parameter patterns. By defining uncertain parameter patterns only in the various subdomains the number of full order simulation required for the derivation of the reduced order models can be reduced drastically. To avoid non-smoothness at the boundaries of the subdomains, the optimal local parameters patterns are projected onto global parameter patterns. The computational effort of the new methodology hardly increases when the number of parameter patterns increases. The number of training models depends primarily on the maximum number of local parameters in a subdomain, which can be decreased by refining the domain decomposition. We apply the new algorithm to a large-scale reservoir model parameter estimation problem. In this application 282 parameters could be estimated using only 90 full order model runs.
ISSN:0021-9991
1090-2716
DOI:10.1016/j.jcp.2020.109867