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

机译:初始数据不光滑的抛物型积分微分方程两种混合有限元方法的最优误差估计

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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 L~2-error estimates are derived for semidiscrete approximations, when the initial condition is in L~2. 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 L~2, which improves upon the results available in the literature.
机译:在本文的第一部分中,提出了一种新的混合方法,并针对初始数据不平滑的抛物线积分微分方程(PIDE)进行了分析。与用于PIDE的标准混合方法相比,本方法不依赖于使用解析算子的重新制定。基于能量参数并结合重复使用积分算子,并且不使用抛物线型对偶技术,当初始条件为L〜2时,可以得出半离散近似的最优L〜2-误差估计。由于存在积分项,进一步可以看出,负范数估计在我们的误差分析中起着至关重要的作用。而且,所提出的分析遵循证明技术的精神,该证明技术用于推导具有平滑数据的PIDE的有限元近似的最佳误差估计,因此,它将两种理论统一起来,即,一种理论用于平滑数据,另一种理论用于非平滑数据。最后,我们用粗糙的初始数据将所提出的分析扩展到用于PIDE的标准混合方法,并在L〜2中提供最佳误差估计,从而改进了文献中可获得的结果。

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