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A QSC method for fractional subdiffusion equations with fractional boundary conditions and its application in parameters identification

机译:具有分数边界条件的分数次扩散方程的QSC方法及其在参数辨识中的应用

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摘要

A quadratic spline collocation (QSC) method combined with L1 time discretization, named QSC-L1, is proposed to solve fractional subdiffusion equations with artificial boundary conditions. A novel norm-based stability and convergence analysis is carefully discussed, which shows that the QSC-L1 method is unconditionally stable in a discrete space-time norm, and has a convergence order O(τ~(2-α) + h~2), where τ and h are the temporal and spatial step sizes, respectively. Then, based on fast evaluation of the Caputo fractional derivative (see, Jiang et al., 2017), a fast version of QSC-L1 which is called QSC-FL1 is proposed to improve the computational efficiency. Two numerical examples are provided to support the theoretical results. Furthermore, an inverse problem is considered, in which some parameters of the fractional subdiffusion equations need to be identified. A Levenberg-Marquardt (L-M) method equipped with the QSC-FL1 method is developed for solving the inverse problem. Numerical tests show the effectiveness of the method even for the case that the observation data is contaminated by some levels of random noise.
机译:提出了一种结合L1时间离散化的二次样条配点(QSC)方法,称为QSC-L1,用于求解带有人工边界条件的分数次扩散方程。仔细讨论了一种新的基于范数的稳定性和收敛性分析,结果表明,QSC-L1方法在离散时空范数中是无条件稳定的,并且收敛阶为O(τ〜(2-α)+ h〜2 ),其中τ和h分别是时间和空间步长。然后,基于Caputo分数阶导数的快速评估(请参见Jiang等人,2017),提出了一种快速版本的QSC-L1,即QSC-FL1,以提高计算效率。提供了两个数值示例来支持理论结果。此外,考虑了反问题,其中需要确定分数次扩散方程的一些参数。为解决反问题,开发了配备QSC-FL1方法的Levenberg-Marquardt(L-M)方法。数值测试证明了该方法的有效性,即使在观测数据受到某种程度的随机噪声污染的情况下也是如此。

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