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    Optimal Error Estimates of Two Mixed Finite Element Methods for Parabolic Integro-Differential Equations with Nonsmooth Initial Data

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    Type
    Article
    Authors
    Goswami, Deepjyoti
    Pani, Amiya K.
    Yadav, Sangita
    KAUST Grant Number
    KUK-C1-013-04
    Date
    2013-05-01
    Online Publication Date
    2013-05-01
    Print Publication Date
    2013-07
    Permanent link to this record
    http://hdl.handle.net/10754/599091
    
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    Abstract
    In the first part of this article, a new mixed method is proposed and analyzed for parabolic integro-differential equations (PIDE) with nonsmooth initial data. Compared to the standard mixed method for PIDE, the present method does not bank on a reformulation using a resolvent operator. Based on energy arguments combined with a repeated use of an integral operator and without using parabolic type duality technique, optimal L2 L2-error estimates are derived for semidiscrete approximations, when the initial condition is in L2 L2. Due to the presence of the integral term, it is, further, observed that a negative norm estimate plays a crucial role in our error analysis. Moreover, the proposed analysis follows the spirit of the proof techniques used in deriving optimal error estimates for finite element approximations to PIDE with smooth data and therefore, it unifies both the theories, i.e., one for smooth data and other for nonsmooth data. Finally, we extend the proposed analysis to the standard mixed method for PIDE with rough initial data and provide an optimal error estimate in L2, L 2, which improves upon the results available in the literature. © 2013 Springer Science+Business Media New York.
    Citation
    Goswami D, Pani AK, Yadav S (2013) Optimal Error Estimates of Two Mixed Finite Element Methods for Parabolic Integro-Differential Equations with Nonsmooth Initial Data. Journal of Scientific Computing 56: 131–164. Available: http://dx.doi.org/10.1007/s10915-012-9666-8.
    Sponsors
    The first author would like to thank CSIR, Government of India for the financial support. The second author acknowledges the research support of the Department of Science and Technology, Government of India under DST-CNPq Indo-Brazil Project No. DST/INT/Brazil /RPO-05/2007 (Grant No. 490795/2007-2). This publication is also based on the work (AKP) supported in part by Award No. KUK-C1-013-04, made by King Abdullah University of Science and Technology (KAUST). The authors also thank the referees for their valuable suggestions.
    Publisher
    Springer Nature
    Journal
    Journal of Scientific Computing
    DOI
    10.1007/s10915-012-9666-8
    ae974a485f413a2113503eed53cd6c53
    10.1007/s10915-012-9666-8
    Scopus Count
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