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 |
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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 |
format | Article |
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−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 & applications, 2017-03, Vol.28 (3), p.585-595</ispartof><rights>The Natural Computing Applications Forum 2015</rights><rights>Copyright Springer Science & 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. M.</creatorcontrib><creatorcontrib>Huerta-Chua, J.</creatorcontrib><creatorcontrib>Hernandez-Machuca, S. F.</creatorcontrib><creatorcontrib>Mendez-Perez, J. M.</creatorcontrib><creatorcontrib>Morales-Mendoza, L. J.</creatorcontrib><creatorcontrib>Gonzalez-Lee, M.</creatorcontrib><title>Extension of Laplace transform–homotopy perturbation method to solve nonlinear differential equations with variable coefficients defined with Robin boundary conditions</title><title>Neural computing & applications</title><addtitle>Neural Comput & Applic</addtitle><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.</description><subject>Artificial Intelligence</subject><subject>Computational Biology/Bioinformatics</subject><subject>Computational Science and Engineering</subject><subject>Computer Science</subject><subject>Data Mining and Knowledge Discovery</subject><subject>Image Processing and Computer Vision</subject><subject>Nonlinear differential equations</subject><subject>Original Article</subject><subject>Perturbation methods</subject><subject>Probability and Statistics in Computer Science</subject><issn>0941-0643</issn><issn>1433-3058</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><recordid>eNp1kU1uFDEQhS1EJIYkB2BniXVDud1u9yxRFAjSSJEisrbcdplx1GN3bHcgWXEHTsG1OAmeNAs2rGpR33v18wh5w-AdA5DvM4BoWQNMNC0M0Dy9IBvWcd5wEMNLsoFtV7t9x1-R1znfAUDXD2JDfl1-Lxiyj4FGR3d6nrRBWpIO2cV0-P3j5z4eYonzI50xlSWNuhzhA5Z9tLREmuP0gDTEMPmAOlHrncOEoXg9UbxfnvlMv_mypw86eT1OSE1E57zxFcvUoqtSuyI3cfSBjnEJVqfHCgbrnx3OyInTU8bzv_WU3H68_HJx1eyuP32--LBrDGd9aVgPW9t3YGQ9nzGG2vARWolSdNINhjHTI--hNR1IxoXsUHDrBLr6ELBbfkrerr5zivcL5qLu4pJCHanYMICUgvVHiq2USTHnhE7NyR_qxoqBOiai1kRUTUQdE1FPVdOumlzZ8BXTP87_Ff0BgjmUTg</recordid><startdate>20170301</startdate><enddate>20170301</enddate><creator>Filobello-Nino, U.</creator><creator>Vazquez-Leal, H.</creator><creator>Khan, Yasir</creator><creator>Sandoval-Hernandez, M.</creator><creator>Perez-Sesma, A.</creator><creator>Sarmiento-Reyes, A.</creator><creator>Benhammouda, Brahim</creator><creator>Jimenez-Fernandez, V. M.</creator><creator>Huerta-Chua, J.</creator><creator>Hernandez-Machuca, S. F.</creator><creator>Mendez-Perez, J. M.</creator><creator>Morales-Mendoza, L. J.</creator><creator>Gonzalez-Lee, M.</creator><general>Springer London</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope></search><sort><creationdate>20170301</creationdate><title>Extension of Laplace transform–homotopy perturbation method to solve nonlinear differential equations with variable coefficients defined with Robin boundary conditions</title><author>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.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c316t-1609d640c7143111eac3b027e7547f8c11c6e3602c40713574e53df5ef4680d93</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Artificial Intelligence</topic><topic>Computational Biology/Bioinformatics</topic><topic>Computational Science and Engineering</topic><topic>Computer Science</topic><topic>Data Mining and Knowledge Discovery</topic><topic>Image Processing and Computer Vision</topic><topic>Nonlinear differential equations</topic><topic>Original Article</topic><topic>Perturbation methods</topic><topic>Probability and Statistics in Computer Science</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><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. M.</creatorcontrib><creatorcontrib>Huerta-Chua, J.</creatorcontrib><creatorcontrib>Hernandez-Machuca, S. F.</creatorcontrib><creatorcontrib>Mendez-Perez, J. M.</creatorcontrib><creatorcontrib>Morales-Mendoza, L. J.</creatorcontrib><creatorcontrib>Gonzalez-Lee, M.</creatorcontrib><collection>CrossRef</collection><jtitle>Neural computing & applications</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Filobello-Nino, U.</au><au>Vazquez-Leal, H.</au><au>Khan, Yasir</au><au>Sandoval-Hernandez, M.</au><au>Perez-Sesma, A.</au><au>Sarmiento-Reyes, A.</au><au>Benhammouda, Brahim</au><au>Jimenez-Fernandez, V. M.</au><au>Huerta-Chua, J.</au><au>Hernandez-Machuca, S. F.</au><au>Mendez-Perez, J. M.</au><au>Morales-Mendoza, L. J.</au><au>Gonzalez-Lee, M.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Extension of Laplace transform–homotopy perturbation method to solve nonlinear differential equations with variable coefficients defined with Robin boundary conditions</atitle><jtitle>Neural computing & applications</jtitle><stitle>Neural Comput & Applic</stitle><date>2017-03-01</date><risdate>2017</risdate><volume>28</volume><issue>3</issue><spage>585</spage><epage>595</epage><pages>585-595</pages><issn>0941-0643</issn><eissn>1433-3058</eissn><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.</abstract><cop>London</cop><pub>Springer London</pub><doi>10.1007/s00521-015-2080-z</doi><tpages>11</tpages></addata></record> |
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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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