两类具有非局部边界条件的分数阶偏微分方程的Pell小波优化方法

IF 3.1 3区 计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Sedigheh Sabermahani , Parisa Rahimkhani , Yadollah Ordokhani
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引用次数: 0

摘要

研究具有非定域条件的初值问题是很重要的,因为它们在物理学和其他应用数学领域都有应用。本文提出了求解两类具有非局部边界条件的分数阶偏微分方程的混合格式,即分数阶反应扩散方程(F-RDEs)和分数阶双曲型偏微分方程(FH-PDEs)。我们开发了一种新的利用Pell小波函数的计算技术。为此,我们给出了积分和Riemann-Liouville分数阶积分的一个导数伪操作矩阵和一个额外的伪操作矩阵,并借助优化和配置方法设计了所需的方法。使用Mathematica软件中的FindRoot包解决了由该技术产生的系统。我们还进行了几个数值实验来验证所建议策略的准确性和优越性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Pell wavelet-optimization procedure for two classes of fractional partial differential equations with nonlocal boundary conditions
Studying initial value problems with nonlocal conditions is important because they have applications in physics and other areas of applied mathematics. This manuscript presents a hybrid scheme for solving two classes of fractional partial differential equations with nonlocal boundary conditions (N-BCs), namely fractional-order reaction–diffusion equations (F-RDEs), and fractional-order hyperbolic partial differential equations (FH-PDEs). We develop a new computational technique that employs Pell wavelet functions. To this end, we present a derivative pseudo-operational matrix and an extra pseudo-operational matrix for integral and Riemann–Liouville fractional integration and design the desired method with the help of optimization and collocation methods. The systems resulting from this technique are solved using the FindRoot package in Mathematica software. We also perform several numerical experiments to validate the accuracy and superiority of the suggested strategy.
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来源期刊
Journal of Computational Science
Journal of Computational Science COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-COMPUTER SCIENCE, THEORY & METHODS
CiteScore
5.50
自引率
3.00%
发文量
227
审稿时长
41 days
期刊介绍: Computational Science is a rapidly growing multi- and interdisciplinary field that uses advanced computing and data analysis to understand and solve complex problems. It has reached a level of predictive capability that now firmly complements the traditional pillars of experimentation and theory. The recent advances in experimental techniques such as detectors, on-line sensor networks and high-resolution imaging techniques, have opened up new windows into physical and biological processes at many levels of detail. The resulting data explosion allows for detailed data driven modeling and simulation. This new discipline in science combines computational thinking, modern computational methods, devices and collateral technologies to address problems far beyond the scope of traditional numerical methods. Computational science typically unifies three distinct elements: • Modeling, Algorithms and Simulations (e.g. numerical and non-numerical, discrete and continuous); • Software developed to solve science (e.g., biological, physical, and social), engineering, medicine, and humanities problems; • Computer and information science that develops and optimizes the advanced system hardware, software, networking, and data management components (e.g. problem solving environments).
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