Extension of Laplace transform–homotopy perturbation method to solve nonlinear differential equations with variable coefficients defined with Robin boundary conditions

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...

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Veröffentlicht in:Neural computing & applications 2017-03, Vol.28 (3), p.585-595
Hauptverfasser: Filobello-Nino, U., Vazquez-Leal, H., Khan, Yasir, Sandoval-Hernandez, M., Perez-Sesma, A., Sarmiento-Reyes, A., Benhammouda, Brahim, Jimenez-Fernandez, V. M., Huerta-Chua, J., Hernandez-Machuca, S. F., Mendez-Perez, J. M., Morales-Mendoza, L. J., Gonzalez-Lee, M.
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container_end_page 595
container_issue 3
container_start_page 585
container_title Neural computing & applications
container_volume 28
creator Filobello-Nino, U.
Vazquez-Leal, H.
Khan, Yasir
Sandoval-Hernandez, M.
Perez-Sesma, A.
Sarmiento-Reyes, A.
Benhammouda, Brahim
Jimenez-Fernandez, V. M.
Huerta-Chua, J.
Hernandez-Machuca, S. F.
Mendez-Perez, J. M.
Morales-Mendoza, L. J.
Gonzalez-Lee, M.
description 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.
doi_str_mv 10.1007/s00521-015-2080-z
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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.</description><identifier>ISSN: 0941-0643</identifier><identifier>EISSN: 1433-3058</identifier><identifier>DOI: 10.1007/s00521-015-2080-z</identifier><language>eng</language><publisher>London: Springer London</publisher><subject>Artificial Intelligence ; Computational Biology/Bioinformatics ; Computational Science and Engineering ; Computer Science ; Data Mining and Knowledge Discovery ; Image Processing and Computer Vision ; Nonlinear differential equations ; Original Article ; Perturbation methods ; Probability and Statistics in Computer Science</subject><ispartof>Neural computing &amp; applications, 2017-03, Vol.28 (3), p.585-595</ispartof><rights>The Natural Computing Applications Forum 2015</rights><rights>Copyright Springer Science &amp; Business Media 2017</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c316t-1609d640c7143111eac3b027e7547f8c11c6e3602c40713574e53df5ef4680d93</citedby><cites>FETCH-LOGICAL-c316t-1609d640c7143111eac3b027e7547f8c11c6e3602c40713574e53df5ef4680d93</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s00521-015-2080-z$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00521-015-2080-z$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27922,27923,41486,42555,51317</link.rule.ids></links><search><creatorcontrib>Filobello-Nino, U.</creatorcontrib><creatorcontrib>Vazquez-Leal, H.</creatorcontrib><creatorcontrib>Khan, Yasir</creatorcontrib><creatorcontrib>Sandoval-Hernandez, M.</creatorcontrib><creatorcontrib>Perez-Sesma, A.</creatorcontrib><creatorcontrib>Sarmiento-Reyes, A.</creatorcontrib><creatorcontrib>Benhammouda, Brahim</creatorcontrib><creatorcontrib>Jimenez-Fernandez, V. 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subjects Artificial Intelligence
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Data Mining and Knowledge Discovery
Image Processing and Computer Vision
Nonlinear differential equations
Original Article
Perturbation methods
Probability and Statistics in Computer Science
title Extension of Laplace transform–homotopy perturbation method to solve nonlinear differential equations with variable coefficients defined with Robin boundary conditions
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