Discontinuous Galerkin approximations to elliptic and parabolic problems with a Dirac line source

IF 0.6 4区 数学 Q4 STATISTICS & PROBABILITY
Rami Masri, Boqian Shen, Beatrice Riviere
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引用次数: 0

Abstract

The analyses of interior penalty discontinuous Galerkin methods of any order k for solving elliptic and parabolic problems with Dirac line sources are presented. For the steady state case, we prove convergence of the method by deriving a priori error estimates in the L 2 norm and in weighted energy norms. In addition, we prove almost optimal local error estimates in the energy norm for any approximation order. Further, almost optimal local error estimates in the L 2 norm are obtained for the case of piecewise linear approximations whereas suboptimal error bounds in the L 2 norm are shown for any polynomial degree. For the time-dependent case, convergence of semi-discrete and of backward Euler fully discrete scheme is established by proving error estimates in L 2 in time and in space. Numerical results for the elliptic problem are added to support the theoretical results.
带狄拉克线源的椭圆型和抛物型问题的不连续伽辽金近似
分析了求解带Dirac线源的椭圆型和抛物型问题的任意阶k内罚不连续伽辽金方法。对于稳态情况,我们通过推导l2范数和加权能量范数的先验误差估计来证明该方法的收敛性。此外,我们还证明了对任意近似阶的能量范数的几乎最优局部误差估计。此外,对于分段线性近似的情况,得到了l2范数中几乎最优的局部误差估计,而对于任何多项式次,l2范数中的次优误差界都得到了显示。对于时变情况,通过证明l2在时间和空间上的误差估计,证明了半离散格式和后向欧拉完全离散格式的收敛性。文中还加入了椭圆型问题的数值结果来支持理论结果。
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来源期刊
Esaim-Probability and Statistics
Esaim-Probability and Statistics STATISTICS & PROBABILITY-
CiteScore
1.00
自引率
0.00%
发文量
14
审稿时长
>12 weeks
期刊介绍: The journal publishes original research and survey papers in the area of Probability and Statistics. It covers theoretical and practical aspects, in any field of these domains. Of particular interest are methodological developments with application in other scientific areas, for example Biology and Genetics, Information Theory, Finance, Bioinformatics, Random structures and Random graphs, Econometrics, Physics. Long papers are very welcome. Indeed, we intend to develop the journal in the direction of applications and to open it to various fields where random mathematical modelling is important. In particular we will call (survey) papers in these areas, in order to make the random community aware of important problems of both theoretical and practical interest. We all know that many recent fascinating developments in Probability and Statistics are coming from "the outside" and we think that ESAIM: P&S should be a good entry point for such exchanges. Of course this does not mean that the journal will be only devoted to practical aspects.
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