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Certified Rapid Solution of Parametrized Linear Elliptic Equations: Application to Parameter Estimation

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dc.contributor.author Nguyen, N. C.
dc.contributor.author Liu, Guirong
dc.contributor.author Patera, Anthony T.
dc.date.accessioned 2004-12-10T14:42:50Z
dc.date.available 2004-12-10T14:42:50Z
dc.date.issued 2005-01
dc.identifier.uri http://hdl.handle.net/1721.1/7375
dc.description.abstract We present a technique for the rapid and reliable evaluation of linear-functional output of elliptic partial differential equations with affine parameter dependence. The essential components are (i) rapidly uniformly convergent reduced-basis approximations — Galerkin projection onto a space WN spanned by solutions of the governing partial differential equation at N (optimally) selected points in parameter space; (ii) a posteriori error estimation — relaxations of the residual equation that provide inexpensive yet sharp and rigorous bounds for the error in the outputs; and (iii) offline/online computational procedures — stratagems that exploit affine parameter dependence to de-couple the generation and projection stages of the approximation process. The operation count for the online stage — in which, given a new parameter value, we calculate the output and associated error bound — depends only on N (typically small) and the parametric complexity of the problem. The method is thus ideally suited to the many-query and real-time contexts. In this paper, based on the technique we develop a robust inverse computational method for very fast solution of inverse problems characterized by parametrized partial differential equations. The essential ideas are in three-fold: first, we apply the technique to the forward problem for the rapid certified evaluation of PDE input-output relations and associated rigorous error bounds; second, we incorporate the reduced-basis approximation and error bounds into the inverse problem formulation; and third, rather than regularize the goodness-of-fit objective, we may instead identify all (or almost all, in the probabilistic sense) system configurations consistent with the available experimental data — well-posedness is reflected in a bounded "possibility region" that furthermore shrinks as the experimental error is decreased. en
dc.description.sponsorship Singapore-MIT Alliance (SMA) en
dc.format.extent 1117342 bytes
dc.format.mimetype application/pdf
dc.language.iso en
dc.relation.ispartofseries High Performance Computation for Engineered Systems (HPCES);
dc.subject Linear elliptic equations en
dc.subject Reduced-basis method en
dc.subject Reduced-basis approximation en
dc.subject A posteriori error estimation en
dc.subject Parameter estimation en
dc.subject Inverse computational method en
dc.subject Possibility region en
dc.title Certified Rapid Solution of Parametrized Linear Elliptic Equations: Application to Parameter Estimation en
dc.type Article en


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