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arXiv 1502.00748math.NAcs.NA
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温控分数阶常微分方程的快速预测-校正方法

Fast predictor-corrector approach for the tempered fractional ordinary differential equations

School of Mathematics and Computer Science, Northwest University for Nationalities(西北民族大学数学与计算机科学学院)

Jingwei Deng, Lijing Zhao, Yujiang Wu

AI总结:

本文提出一种基于'极短记忆'原理的快速预测-校正方法,用于求解温控分数阶常微分方程,通过等分布思想设计算法,验证其有效性及线性增长的计算成本。

摘要:

温控演化方程描述了被困动力学,广泛出现在自然界,例如黏性液体中活体粒子的运动。本文通过挖掘'极短记忆'原理,提出温控分数阶常微分方程的快速预测-校正方法。基于等分布思想的算法被详细描述;其有效性和低计算成本,即计算成本与时间t线性增长,通过数值实验得到验证。

英文摘要:

The tempered evolution equation describes the trapped dynamics, widely appearing in nature, e.g., the motion of living particles in viscous liquid. This paper proposes the fast predictor-corrector approach for the tempered fractional ordinary differential equations by digging out the potential 'very' short memory principle. The algorithms basing on the idea of equidistributing are detailedly described; their effectiveness and low computation cost, being linearly increasing with time $t$, are numerically demonstrated.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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