Rida Saad Z, Arafa Anas A M, Hussein Hussein S, Ameen Ismail Gad, Mostafa Marwa M M
Department of Mathematics, Faculty of Science, South Valley University, Qena, 83523, Egypt.
Department of Mathematics, College of Science, Qassim University, Buraydah, Saudi Arabia.
Sci Rep. 2024 Dec 23;14(1):30596. doi: 10.1038/s41598-024-79247-9.
The Klein-Gordon problem (KGP) is one of the interesting models that appear in many scientific phenomena. These models are characterized by memory effects, which provide insight into complex phenomena in the fields of physics. In this regard, we propose a new robust algorithm called the confluent Bernoulli approach with residual power series scheme (CBCA-RPSS) to give an approximate solution for the fractional nonlinear KGP. The convergence, uniqueness and error analysis of the proposed method are discussed in detail. A comparison of the numerical results obtained by CBCA-RPSS with the results obtained by some well-known algorithms is presented. Numerical simulations using base errors indicate that CBCA-RPSS is an accurate and efficient technique and thus can be used to solve linear and nonlinear fractional models in physics and engineering. All the numerical results for the studied problems were obtained through implementation codes in Matlab R2017b.
克莱因-戈登问题(KGP)是出现在许多科学现象中的有趣模型之一。这些模型具有记忆效应,这为洞察物理领域的复杂现象提供了思路。在这方面,我们提出了一种新的稳健算法,称为具有残差幂级数格式的合流伯努利方法(CBCA-RPSS),以给出分数阶非线性KGP的近似解。详细讨论了所提方法的收敛性、唯一性和误差分析。给出了CBCA-RPSS获得的数值结果与一些知名算法获得的结果的比较。使用基本误差的数值模拟表明,CBCA-RPSS是一种准确且高效的技术,因此可用于求解物理和工程中的线性和非线性分数阶模型。所研究问题的所有数值结果都是通过在Matlab R2017b中实现的代码获得的。