Proper Generalized Decomposition model reduction in the Bayesian framework for solving inverse heat transfer problems. Issue 2 (1st February 2017)

• Record Type: Journal Article
• Authors: Berger, Julien; Orlande, Helcio R. B.; Mendes, Nathan
• Is Part Of: Inverse problems in science and engineering. Volume 25:Issue 2(2017)
• Page Start: 260
• Page End: 278
• Publication Date: 2017-02-01
• Subjects: Bayesian inference -- approximation error model -- model reduction methods -- proper generalized decomposition; Engineering mathematics -- Periodicals; Inverse problems (Differential equations) -- Periodicals; 620.001515357
• DOI: 10.1080/17415977.2016.1160395
• Languages: English
• ISSNs: 1741-5977

Abstract

Abstract : In this paper, the proper generalized decomposition (PGD) is used for model reduction in the solution of an inverse heat conduction problem within the Bayesian framework. Two PGD reduced order models are proposed and the approximation Error model (AEM) is applied to account for the errors between the complete and the reduced models. For the first PGD model, the direct problem solution is computed considering a separate representation of each coordinate of the problem during the process of solving the inverse problem. On the other hand, the second PGD model is based on a generalized solution integrating the unknown parameter as one of the coordinates of the decomposition. For the second PGD model, the reduced solution of the direct problem is computed before the inverse problem within the parameter space provided by the prior information about the parameters, which is required to be proper. These two reduced models are evaluated in terms of accuracy and reduction of the computational time on a transient three-dimensional two region inverse heat transfer problem. In fact, both reduced models result on substantial reduction of the computational time required for the solution of the inverse problem, and provide accurate estimates for the unknown parameter due to the application of the approximation error model approach.