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dc.contributor.authorEfendiev, Yalchin R.
dc.contributor.authorGalvis, Juan
dc.contributor.authorWu, Xiao-Hui
dc.date.accessioned2016-02-25T13:43:40Z
dc.date.available2016-02-25T13:43:40Z
dc.date.issued2011-02
dc.identifier.citationEfendiev Y, Galvis J, Wu X-H (2011) Multiscale finite element methods for high-contrast problems using local spectral basis functions. Journal of Computational Physics 230: 937–955. Available: http://dx.doi.org/10.1016/j.jcp.2010.09.026.
dc.identifier.issn0021-9991
dc.identifier.doi10.1016/j.jcp.2010.09.026
dc.identifier.urihttp://hdl.handle.net/10754/598916
dc.description.abstractIn this paper we study multiscale finite element methods (MsFEMs) using spectral multiscale basis functions that are designed for high-contrast problems. Multiscale basis functions are constructed using eigenvectors of a carefully selected local spectral problem. This local spectral problem strongly depends on the choice of initial partition of unity functions. The resulting space enriches the initial multiscale space using eigenvectors of local spectral problem. The eigenvectors corresponding to small, asymptotically vanishing, eigenvalues detect important features of the solutions that are not captured by initial multiscale basis functions. Multiscale basis functions are constructed such that they span these eigenfunctions that correspond to small, asymptotically vanishing, eigenvalues. We present a convergence study that shows that the convergence rate (in energy norm) is proportional to (H/Λ*)1/2, where Λ* is proportional to the minimum of the eigenvalues that the corresponding eigenvectors are not included in the coarse space. Thus, we would like to reach to a larger eigenvalue with a smaller coarse space. This is accomplished with a careful choice of initial multiscale basis functions and the setup of the eigenvalue problems. Numerical results are presented to back-up our theoretical results and to show higher accuracy of MsFEMs with spectral multiscale basis functions. We also present a hierarchical construction of the eigenvectors that provides CPU savings. © 2010.
dc.description.sponsorshipThe work of Y.E. and J.G. is partially supported by Award Number KUS-C1-016-04, made by King Abdullah University of Science and Technology (KAUST). Y.E.'s research is partially supported by NSF (0724704, 0811180, 0934837) and DOE. We would like to thank the anonymous reviewers for their suggestions that helped to improve the paper.
dc.publisherElsevier BV
dc.subjectHigh contrast
dc.subjectMultiscale finite element
dc.subjectPorous media
dc.subjectSpectral
dc.titleMultiscale finite element methods for high-contrast problems using local spectral basis functions
dc.typeArticle
dc.identifier.journalJournal of Computational Physics
dc.contributor.institutionTexas A and M University, College Station, United States
dc.contributor.institutionExxonMobil, Irving, United States
kaust.grant.numberKUS-C1-016-04


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