Optimal Combination of EEFs for the Model Reduction of Nonlinear Partial Differential Equations

Proper orthogonal decomposition is a popular approach for determining the principal spatial structures from the measured data. Generally, model reduction using empirical eigenfunctions (EEFs) can generate a relatively low-dimensional model among all linear expansions. However, the neglectful modes r...

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Main Authors: Jun Shuai, Xuli Han
Format: Article
Language:English
Published: Wiley 2013-01-01
Series:Journal of Applied Mathematics
Online Access:http://dx.doi.org/10.1155/2013/347248
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author Jun Shuai
Xuli Han
author_facet Jun Shuai
Xuli Han
author_sort Jun Shuai
collection DOAJ
description Proper orthogonal decomposition is a popular approach for determining the principal spatial structures from the measured data. Generally, model reduction using empirical eigenfunctions (EEFs) can generate a relatively low-dimensional model among all linear expansions. However, the neglectful modes representing only a tiny amount of energy will be crucial in the modeling for certain type of nonlinear partial differential equations (PDEs). In this paper, an optimal combination of EEFs is proposed for model reduction of nonlinear partial differential equations (PDEs), obtained by the basis function transformation from the initial EEFs. The transformation matrix is derived from straightforward optimization techniques. The present new EEFs can keep the dynamical information of neglectful modes and generate a lower-dimensional and more precise dynamical system for the PDEs. The numerical example shows its effectiveness and feasibility for model reduction of the nonlinear PDEs.
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publishDate 2013-01-01
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series Journal of Applied Mathematics
spelling doaj-art-a663f733a3624717a1a6ddf3d2a149c52025-02-03T01:09:49ZengWileyJournal of Applied Mathematics1110-757X1687-00422013-01-01201310.1155/2013/347248347248Optimal Combination of EEFs for the Model Reduction of Nonlinear Partial Differential EquationsJun Shuai0Xuli Han1School of Mathematics and Statistics, Central South University, Changsha 410083, ChinaSchool of Mathematics and Statistics, Central South University, Changsha 410083, ChinaProper orthogonal decomposition is a popular approach for determining the principal spatial structures from the measured data. Generally, model reduction using empirical eigenfunctions (EEFs) can generate a relatively low-dimensional model among all linear expansions. However, the neglectful modes representing only a tiny amount of energy will be crucial in the modeling for certain type of nonlinear partial differential equations (PDEs). In this paper, an optimal combination of EEFs is proposed for model reduction of nonlinear partial differential equations (PDEs), obtained by the basis function transformation from the initial EEFs. The transformation matrix is derived from straightforward optimization techniques. The present new EEFs can keep the dynamical information of neglectful modes and generate a lower-dimensional and more precise dynamical system for the PDEs. The numerical example shows its effectiveness and feasibility for model reduction of the nonlinear PDEs.http://dx.doi.org/10.1155/2013/347248
spellingShingle Jun Shuai
Xuli Han
Optimal Combination of EEFs for the Model Reduction of Nonlinear Partial Differential Equations
Journal of Applied Mathematics
title Optimal Combination of EEFs for the Model Reduction of Nonlinear Partial Differential Equations
title_full Optimal Combination of EEFs for the Model Reduction of Nonlinear Partial Differential Equations
title_fullStr Optimal Combination of EEFs for the Model Reduction of Nonlinear Partial Differential Equations
title_full_unstemmed Optimal Combination of EEFs for the Model Reduction of Nonlinear Partial Differential Equations
title_short Optimal Combination of EEFs for the Model Reduction of Nonlinear Partial Differential Equations
title_sort optimal combination of eefs for the model reduction of nonlinear partial differential equations
url http://dx.doi.org/10.1155/2013/347248
work_keys_str_mv AT junshuai optimalcombinationofeefsforthemodelreductionofnonlinearpartialdifferentialequations
AT xulihan optimalcombinationofeefsforthemodelreductionofnonlinearpartialdifferentialequations