Simulating time delays and space–time memory interactions: An analytical approach

Q1 Mathematics Partial Differential Equations in Applied Mathematics Pub Date : 2024-09-01 Epub Date: 2024-08-22 DOI:10.1016/j.padiff.2024.100881
Imad Jaradat
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Abstract

This study introduces a novel analytical framework to explore the effects of Caputo spatial and temporal memory indices combined with a proportional time delay on (non)linear (1+2)-dimensional evolutionary models. The solution is expressed as a Cauchy product of an absolutely convergent series that effectively captures the dynamics of these parameters. By extending the differential transform method into higher-dimensional fractional space, we reformulate the evolution equation as a (non)linear higher-order recurrence relation, which enables the precise determination of fractional series coefficients. Our findings show that Caputo derivatives and time delay significantly impact the system’s behavior, with graphical analysis revealing a continuous transition from a stationary to an integer state solution. The study also identifies a quantitative analogy between the Caputo-time fractional derivative and proportional time delay that highlights the role of Caputo derivatives as memory indices. This method has proven highly effective in deriving solutions for fractional higher-dimensional extensions of evolutionary equations.

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模拟时间延迟和时空记忆相互作用:分析方法
本研究引入了一个新颖的分析框架,以探讨卡普托空间和时间记忆指数与比例时间延迟相结合对(非)线性(1+2)维进化模型的影响。解被表示为绝对收敛级数的考奇乘积,它能有效捕捉这些参数的动态变化。通过将微分变换方法扩展到高维分数空间,我们将演化方程重新表述为(非)线性高阶递推关系,从而能够精确确定分数序列系数。我们的研究结果表明,卡普托导数和时间延迟对系统行为有显著影响,图形分析显示了从静止状态到整数状态解的连续过渡。研究还发现了卡普托时间分数导数和比例时间延迟之间的定量类比,突出了卡普托导数作为记忆指数的作用。事实证明,这种方法在推导进化方程的分数高维扩展解方面非常有效。
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来源期刊
CiteScore
6.20
自引率
0.00%
发文量
138
审稿时长
14 weeks
期刊最新文献
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