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    Adaptive Multilevel Methods with Local Smoothing for $H^1$- and $H^{\mathrm{curl}}$-Conforming High Order Finite Element Methods

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    Type
    Article
    Authors
    Janssen, Bärbel
    Kanschat, Guido
    KAUST Grant Number
    KUS-C1-016-04
    Date
    2011-01
    Permanent link to this record
    http://hdl.handle.net/10754/597458
    
    Metadata
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    Abstract
    A multilevel method on adaptive meshes with hanging nodes is presented, and the additional matrices appearing in the implementation are derived. Smoothers of overlapping Schwarz type are discussed; smoothing is restricted to the interior of the subdomains refined to the current level; thus it has optimal computational complexity. When applied to conforming finite element discretizations of elliptic problems and Maxwell equations, the method's convergence rates are very close to those for the nonadaptive version. Furthermore, the smoothers remain efficient for high order finite elements. We discuss the implementation in a general finite element code using the example of the deal.II library. © 2011 Societ y for Industrial and Applied Mathematics.
    Citation
    Janssen B, Kanschat G (2011) Adaptive Multilevel Methods with Local Smoothing for $H^1$- and $H^{\mathrm{curl}}$-Conforming High Order Finite Element Methods. SIAM Journal on Scientific Computing 33: 2095–2114. Available: http://dx.doi.org/10.1137/090778523.
    Sponsors
    This author's work was supported by the German Research Association (DFG) and the International Graduate College IGK 710.This author's work was supported by the National Science Foundation under grants DMS-0713829 and DMS-0810387 and by the King Abdullah University of Science and Technology (KAUST) through award KUS-C1-016-04. Some of the experiments were performed during a visit to the Institute of Mathematics and its Applications in Minneapolis.
    Publisher
    Society for Industrial & Applied Mathematics (SIAM)
    Journal
    SIAM Journal on Scientific Computing
    DOI
    10.1137/090778523
    ae974a485f413a2113503eed53cd6c53
    10.1137/090778523
    Scopus Count
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