Approximate Series Solution of Nonlinear, Fractional Klein-Gordon Equations Using Fractional Reduced Differential Transform Method
Eman Abuteen1, Asad Freihat1, Mohammed Al-Smadi1, Hammad Khalil2 & Rahmat Ali Khan2 Show affiliations
- Al-Balqa Applied University, Jordan
- University of Malakand, Pakistan
Journal of Mathematics and Statistics, Volume 12, Issue 1, pp. 23–33 · doi.org/10.3844/jmssp.2016.23.33
Abstract
This analysis proposes an analytical-numerical approach for providing solutions of a class of nonlinear fractional Klein-Gordon equation subjected to appropriate initial conditions in Caputo sense by using the Fractional Reduced Differential Transform Method (FRDTM). This technique provides the solutions very accurately and efficiently in convergent series formula with easily computable coefficients. The behavior of the approximate series solution for different values of fractional-order α is shown graphically. A comparative study is presented between the FRDTM and Implicit Runge-Kutta approach to illustrate the efficiency and reliability of the proposed technique. Our numerical investigations indicate that the FRDTM is simple, powerful mathematical tool and fully compatible with the complexity of such problems.
Licence and reuse
Open access. This article is licensed under a Creative Commons Attribution 3.0 Licence (CC BY 3.0), as printed on the article, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original authors and the source. No permission is needed.
How to reuse this articleResponses (0)
About this article
Abuteen, E., Freihat, A., Al-Smadi, M., Khalil, H. & Khan, R. A. (2016). Approximate Series Solution of Nonlinear, Fractional Klein-Gordon Equations Using Fractional Reduced Differential Transform Method. Journal of Mathematics and Statistics, 12(1), 23–33. https://doi.org/10.3844/jmssp.2016.23.33
- Licence
- CC BY 3.0
- Issue
- Volume 12, Issue 1
- Policies
- Editorial independenceUse of generative AIData availabilityAppeals and complaints
- Version
- Version of record
