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The novel Leal-polynomials for the multi-expansive approximation of nonlinear differential equations

机译:非线性微分方程多元展开式的新颖Leal多项式

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摘要

This work presents the novel Leal-polynomials (LP) for the approximation of nonlinear differential equations of different kind. The main characteristic of LPs is that they satisfy multiple expansion points and its derivatives as a mechanism to replicate behaviour of the nonlinear problem, giving more accuracy within the region of interest. Therefore, the main contribution of this work is that LP satisfies the successive derivatives in some specific points, resulting more accurate polynomials than Taylor expansion does for the same degree of their respective polynomials. Such characteristic makes of LPs a handy and powerful tool to approximate different kind of differential equations including: singular problems, initial condition and boundary-valued problems, equations with discontinuities, coupled differential equations, high-order equations, among others. Additionally, we show how the process to obtain the polynomials is straightforward and simple to implement; generating a compact, and easy to compute, expression. Even more, we present the process to approximate Gelfand's equation, an equation of an isothermal reaction, a model for chronic myelogenous leukemia, Thomas-Fermi equation, and a high order nonlinear differential equations with discontinuities getting, as result, accurate, fast and compact approximate solutions. In addition, we present the computational convergence and error studies for LPs resulting convergent polynomials and error tendency to zero as the order of LPs increases for all study cases. Finally, a study of CPU time shows that LPs require a few nano-seconds to be evaluated, which makes them suitable for intensive computing applications.
机译:这项工作提出了新颖的Leal多项式(LP),用于近似不同种类的非线性微分方程。 LP的主要特征是它们满足多个扩展点及其导数,作为复制非线性问题行为的机制,从而在感兴趣区域内提供了更高的准确性。因此,这项工作的主要贡献在于,LP在某些特定点上满足了连续导数,因此在相同次数的多项式上,比泰勒展开式更精确的多项式。 LP的这种特性使LPs成为方便且强大的工具,可以近似各种微分方程,其中包括:奇异问题,初始条件和边值问题,具有间断性的方程,耦合的微分方程,高阶方程等。此外,我们展示了获得多项式的过程如何简单,易于实现;生成紧凑且易于计算的表达式。更进一步,我们介绍了近似Gelfand方程,等温反应方程,慢性粒细胞白血病模型,Thomas-Fermi方程以及具有不连续点的高阶非线性微分方程的过程,结果是精确,快速而紧凑近似解。此外,对于所有研究案例,我们介绍了LP的计算收敛性和误差研究,从而得出了收敛的多项式,并且随着LP阶数的增加,误差趋向于零。最后,对CPU时间的研究表明,LP需要几纳秒的时间才能被评估,这使其适合于密集型计算应用。

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