Extension of Laplace transform–homotopy perturbation method to solve nonlinear differential equations with variable coefficients defined with Robin boundary conditions. Issue 3 (March 2017)

• Record Type: Journal Article
• Authors: Filobello-Nino, U.; Vazquez-Leal, H.; Khan, Yasir; Sandoval-Hernandez, M.; Perez-Sesma, A.; Sarmiento-Reyes, A.; Benhammouda, Brahim; Jimenez-Fernandez, V.; Huerta-Chua, J.; Hernandez-Machuca, S.; Mendez-Perez, J.; Morales-Mendoza, L.; Gonzalez-Lee, M.
• Is Part Of: Neural computing & applications. Volume 28:Issue 3(2017)
• Page Start: 585
• Page End: 595
• Publication Date: 2017-03
• Subjects: Homotopy perturbation method -- Nonlinear differential equation -- Approximate solutions -- Laplace transform -- Laplace transform–homotopy perturbation method -- Oxygen cell -- Diffusion; Neural networks (Computer science) -- Periodicals; Neural circuitry -- Periodicals; Artificial intelligence -- Periodicals; Neural Networks (Computer) -- Periodicals; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux nerveux -- Périodiques; Intelligence artificielle -- Périodiques; 006.32
• DOI: 10.1007/s00521-015-2080-z
• Languages: English
• ISSNs: 0941-0643

Abstract

Abstract This article proposes the application of Laplace transform–homotopy perturbation method with variable coefficients, in order to find analytical approximate solutions for nonlinear differential equations with variable coefficients. As case study, we present the oxygen diffusion problem in a spherical cell including nonlinear Michaelis–Menten uptake kinetics. It is noteworthy that this important problem introduces the Robin boundary conditions as an additional difficulty. In fact, after comparing figures between approximate and exact solutions, we will see that the proposed solutions are highly accurate. What is more, we will see that the square residual error of our solutions is 1.808511632 × 10−7 and 2.560574954 × 10−10 which confirms the accuracy of the proposed method, taking into account that we will just keep the first-order approximation.