使用细谷多项式方法数值求解非线性亨特-萨克斯顿方程、本杰明-博纳-马霍尼方程和克莱因-戈登方程

Q3 Mathematics
AN Nirmala, S. Kumbinarasaiah
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引用次数: 0

摘要

非线性偏微分方程模型是理论科学,尤其是物理学和数学中必不可少的。要获得解析解,难度极大。因此,科学家们一直在寻找新的计算技术。针对特定的非线性数学模型,如研究广泛的亨特-萨克斯顿方程(HSE)、本杰明-博纳-马霍尼方程(BBME)和克莱因-戈登方程(KGE),我们提出了一种新颖有效的图论多项式方法,即细谷多项式配位法(HPCM)。路径图的正规范化细谷多项式及其运算矩阵是新 HPCM 的函数基础。利用运算矩阵近似法和适当的配位法,将所考虑的非线性模型转化为非线性代数方程系统,从而得到近似解。六个数值实例验证了 HPCM 的生产力。与目前文献中的数值方法相比,其结果明显更有效,几乎与分析解相吻合。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Numerical solution of nonlinear Hunter-Saxton equation, Benjamin-Bona Mahony equation, and Klein-Gordon equation using Hosoya polynomial method

Models of nonlinear partial differential equations are essential in the theoretical sciences, especially Physics and Mathematics. It is exceedingly challenging to arrive at an analytical solution. For this reason, scientists are constantly looking for new computational techniques. For specific nonlinear mathematical models, such as the extensively researched Hunter-Saxton equation (HSE), Benjamin-Bona-Mahony equation (BBME), and Klein-Gordon equation (KGE), we are putting forth a novel and effective graph theoretic polynomial method named the Hosoya polynomial collocation method (HPCM). The orthonormalized Hosoya polynomials of the path graph and their operational matrices serve as the functional foundation for the new HPCM. The considered nonlinear models were transformed into a system of nonlinear algebraic equations using the operational matrix approximation and an appropriate collocation approach to reach the approximate solution. Six numerical examples verified the HPCM's productivity. Compared to the current numerical approaches in the literature, the results are significantly more efficient and almost match the analytical solution.

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来源期刊
Results in Control and Optimization
Results in Control and Optimization Mathematics-Control and Optimization
CiteScore
3.00
自引率
0.00%
发文量
51
审稿时长
91 days
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