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A new algorithm based on Lucas polynomials for approximate solution of 1D and 2D nonlinear generalized Benjamin-Bona-Mahony-Burgers equation

机译:基于Lucas多项式的一维和二维非线性广义Benjamin-Bona-Mahony-Burgers方程近似解的新算法

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In this paper, a new method based on hybridization of Lucas and Fibonacci polynomials is developed for approximate solutions of 1D and 2D nonlinear generalized Benjamin-Bona-Mahony-Burgers equations. Firstly time discretization is made by using finite difference approaches. After that unknown function and its derivatives are expanded to Lucas series. Based on these series expansion, differentiation matrices are derived by utilizing Fibonacci polynomials. By doing so, the solution of the mentioned equations is reduced to the solution of an algebraic system of equations. By solving this system of equations the Lucas series coefficients are obtained. Then substituting these coefficients into Lucas series expansion approximate solutions can be constructed successively. The main goal of this paper is to indicate that Lucas polynomial based method is appropriate for 1D and 2D nonlinear problems. Efficiency and performance of the proposed method are judged on six test problems which consists of the ID and 2D version of mentioned equation by calculating L-2 and L-infinity error norms. Feasibility of the method is verified by obtained accurate results. (C) 2017 Elsevier Ltd. All rights reserved.
机译:本文针对一维和二维非线性广义Benjamin-Bona-Mahony-Burgers方程的近似解,开发了一种基于Lucas和Fibonacci多项式混合的新方法。首先,通过使用有限差分方法进行时间离散化。之后,未知函数及其派生类扩展为Lucas系列。基于这些级数展开,利用斐波那契多项式推导微分矩阵。通过这样做,所提到的方程的解被简化为方程的代数系统的解。通过求解该方程组,可以获得卢卡斯级数系数。然后可以将这些系数代入卢卡斯级数展开式的近似解。本文的主要目的是表明基于卢卡斯多项式的方法适用于一维和二维非线性问题。通过计算L-2和L-无穷大误差范数,在六个测试问题(包括所提及方程的ID和2D版本)上,对所提出方法的效率和性能进行了判断。通过获得准确的结果验证了该方法的可行性。 (C)2017 Elsevier Ltd.保留所有权利。

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