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Efficient numerical algorithms for Riesz-space fractional partial differential equations based on finite difference/operational matrix

机译:基于有限差分/运算矩阵的RIESZ空间分数局部微分方程的高效数值算法

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摘要

In this paper, we construct two efficient numerical schemes by combining the finite difference method and operational matrix method (OMM) to solve Riesz-space fractional diffusion equation (RFDE) and Riesz-space fractional advection-dispersion equation (RFADE) with initial and Dirichlet boundary conditions. We applied matrix transform method (MTM) for discretization of Riesz-space fractional derivative and OMM based on shifted Legendre polynomials (SLP) and shifted Chebyshev polynomial (SCP) of second kind for approximating the time derivatives. The proposed schemes transform the RFDE and RFADE into the system of linear algebraic equations. For a better understanding of the methods, numerical algorithms are also provided for the considered problems. Furthermore, optimal error bound for the numerical solution is derived, and theoretical unconditional stability has been proved with respect to 1.2-norm. The stability of the schemes is also verified numerically. The schemes are observed to be of second-order accurate in space. The effectiveness and accuracy of the schemes are tested by taking two numerical examples of RFDE and RFADE and found to be in good agreement with the exact solutions. It is observed that the numerical schemes are simple, easy to implement, yield high accurate results with both the basis functions. Moreover, the CPU time taken by the schemes with SLP basis is very less as compared to schemes with SCP basis.
机译:在本文中,通过组合有限差分方法和操作矩阵方法(OMM)来构造两个有效的数值方案,以解决初始和Dirichlet的Riesz空间分数扩散方程(RFDE)和RIESZ空间分数平流 - 分散方程(RFADE)边界条件。我们应用了矩阵变换方法(MTM),用于基于移位的图例多项式(SLP)的RIESZ空间分数衍生物和OMM的离散化,以及第二类的SHOWBYSHEV多项式(SCP),以近似时间衍生物。所提出的方案将RFDE和RFADE转换为线性代数方程系统。为了更好地理解方法,还提供了数值算法用于考虑的问题。此外,推导出对数值溶液的最佳误差,并相对于1.2常态证明了理论无条件稳定性。这些方案的稳定性也在数值上验证。观察到该方案是在空间中的二阶准确。通过拍摄RFDE和RFADE的两个数值例子来测试方案的有效性和准确性,发现与确切的解决方案有关良好的一致性。观察到数字方案简单,易于实现,通过基础函数产生高准确的结果。此外,与具有SCP的方案相比,SLP的方案采取的CPU时间非常少。

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