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Numerical algorithm to Caputo type time-space fractional partial differential equations with variable coefficients

机译:具有变系数的Caputo型时间空间分数偏微分方程的数值算法

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This article assigns to a new numerical algorithm as an appropriate tool that deals with the time-space fractional partial differential equations in Caputo sense with variable coefficients. In the current algorithm, firstly, we presume that the approximate solution of the main problem is expandable along space variable via shifted Chebyshev polynomials with time-dependent coefficients. In the second step, we employ operational matrices of space-fractional derivatives to transform (reduce) the expanded problem to a system of time-fractional ordinary differential equations (FODEs) with initial value conditions. Indeed, the solutions of this system are required to obtain the time-dependent coefficients of the mentioned expansion. To solve this system, we define some independent secondary initial value problems and solve them analytically. At the final step, we find an optimal linear combination of this particular solutions to obtain an approximate solution of the main problem such that the residual error function forced to vanish in an average sense over the desired region, and the approximate solution satisfies in initial/boundary conditions of the main problem. The convergence property of the presented algorithm is demonstrated by the residual error analysis. The reliability and accuracy of the new algorithm are confirmed by solving some illustrative test problems. In order to perform a premier analysis with more details for the convergence property of the new algorithm, we compute the observed convergence order indicators in each test problem. Moreover, we evaluate our computed results with other numerical schemes in the literature to emphasize the promising performance of the proposed algorithm.
机译:本文将新的数字算法分配为适当的工具,该工具处理Caputo Sense中的时间空间分数偏微分方程,具有可变系数。在本前算法中,我们假设主要问题的近似解是沿着空间变量可扩展的Chebyshev多项式,其具有时间相关系数。在第二步中,我们采用了空间分数衍生物的运营矩阵,以将扩展问题转换为与初始值条件的时间分数常微分方程(Fode)的扩展问题。实际上,需要该系统的解决方案来获得所述扩展的时间依赖性系数。要解决此系统,我们定义了一些独立的辅助初始值问题并分析解决了它们。在最后一步中,我们发现该特定解决方案的最佳线性组合,以获得主要问题的近似解,使得剩余误差函数被迫在所需区域的平均意义上消失,并且近似解决方案满足初始/主要问题的边界条件。通过剩余误差分析证明了所提出的算法的收敛性。通过解决一些说明性测试问题,确认了新算法的可靠性和准确性。为了对新算法的收敛性的更多细节进行总理分析,我们将观察到的收敛订单指标计算在每个测试问题中。此外,我们通过文献中的其他数值方案评估我们计算的结果,以强调所提出的算法的有希望的性能。

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