Intelligent computing technique based supervised learning for squeezing flow model

Abstract In this study, the unsteady squeezing flow between circular parallel plates (USF-CPP) is investigated through the intelligent computing paradigm of Levenberg–Marquard backpropagation neural networks (LMBNN). Similarity transformation introduces the fluidic system of the governing partial di...

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Autores principales:	Maryam Mabrook Almalki, Eman Salem Alaidarous, Dalal Adnan Maturi, Muhammad Asif Zahoor Raja, Muhammad Shoaib
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/36dd863941894678935a831deb6f6823
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spelling	oai:doaj.org-article:36dd863941894678935a831deb6f68232021-12-02T19:17:04ZIntelligent computing technique based supervised learning for squeezing flow model10.1038/s41598-021-99108-z2045-2322https://doaj.org/article/36dd863941894678935a831deb6f68232021-10-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-99108-zhttps://doaj.org/toc/2045-2322Abstract In this study, the unsteady squeezing flow between circular parallel plates (USF-CPP) is investigated through the intelligent computing paradigm of Levenberg–Marquard backpropagation neural networks (LMBNN). Similarity transformation introduces the fluidic system of the governing partial differential equations into nonlinear ordinary differential equations. A dataset is generated based on squeezing fluid flow system USF-CPP for the LMBNN through the Runge–Kutta method by the suitable variations of Reynolds number and volume flow rate. To attain approximation solutions for USF-CPP to different scenarios and cases of LMBNN, the operations of training, testing, and validation are prepared and then the outcomes are compared with the reference data set to ensure the suggested model’s accuracy. The output of LMBNN is discussed by the mean square error, dynamics of state transition, analysis of error histograms, and regression illustrations.Maryam Mabrook AlmalkiEman Salem AlaidarousDalal Adnan MaturiMuhammad Asif Zahoor RajaMuhammad ShoaibNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Maryam Mabrook Almalki Eman Salem Alaidarous Dalal Adnan Maturi Muhammad Asif Zahoor Raja Muhammad Shoaib Intelligent computing technique based supervised learning for squeezing flow model
description	Abstract In this study, the unsteady squeezing flow between circular parallel plates (USF-CPP) is investigated through the intelligent computing paradigm of Levenberg–Marquard backpropagation neural networks (LMBNN). Similarity transformation introduces the fluidic system of the governing partial differential equations into nonlinear ordinary differential equations. A dataset is generated based on squeezing fluid flow system USF-CPP for the LMBNN through the Runge–Kutta method by the suitable variations of Reynolds number and volume flow rate. To attain approximation solutions for USF-CPP to different scenarios and cases of LMBNN, the operations of training, testing, and validation are prepared and then the outcomes are compared with the reference data set to ensure the suggested model’s accuracy. The output of LMBNN is discussed by the mean square error, dynamics of state transition, analysis of error histograms, and regression illustrations.
format	article
author	Maryam Mabrook Almalki Eman Salem Alaidarous Dalal Adnan Maturi Muhammad Asif Zahoor Raja Muhammad Shoaib
author_facet	Maryam Mabrook Almalki Eman Salem Alaidarous Dalal Adnan Maturi Muhammad Asif Zahoor Raja Muhammad Shoaib
author_sort	Maryam Mabrook Almalki
title	Intelligent computing technique based supervised learning for squeezing flow model
title_short	Intelligent computing technique based supervised learning for squeezing flow model
title_full	Intelligent computing technique based supervised learning for squeezing flow model
title_fullStr	Intelligent computing technique based supervised learning for squeezing flow model
title_full_unstemmed	Intelligent computing technique based supervised learning for squeezing flow model
title_sort	intelligent computing technique based supervised learning for squeezing flow model
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/36dd863941894678935a831deb6f6823
work_keys_str_mv	AT maryammabrookalmalki intelligentcomputingtechniquebasedsupervisedlearningforsqueezingflowmodel AT emansalemalaidarous intelligentcomputingtechniquebasedsupervisedlearningforsqueezingflowmodel AT dalaladnanmaturi intelligentcomputingtechniquebasedsupervisedlearningforsqueezingflowmodel AT muhammadasifzahoorraja intelligentcomputingtechniquebasedsupervisedlearningforsqueezingflowmodel AT muhammadshoaib intelligentcomputingtechniquebasedsupervisedlearningforsqueezingflowmodel
_version_	1718376905441280000

Intelligent computing technique based supervised learning for squeezing flow model

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