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Supervised Learning Algorithm to Study the Magnetohydrodynamic Flow of a Third Grade Fluid for the Analysis of Wire Coating

In the present study, modeling of intelligent numerical computing through Levenberg–Marquardt back propagation-based supervised neural network (LMB-SNN) is incorporated to analyze the magnetohydrodynamic flow of a third grade fluid for wire coating analysis (MHD-TGFWCA). The original mathematical fo...

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Autores principales: Aljohani, Jawaher Lafi, Alaidarous, Eman Salem, Raja, Muhammad Asif Zahoor, Alhothuali, Muhammed Shabab, Shoaib, Muhammad
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8479500/
https://www.ncbi.nlm.nih.gov/pubmed/34603928
http://dx.doi.org/10.1007/s13369-021-06212-3
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author Aljohani, Jawaher Lafi
Alaidarous, Eman Salem
Raja, Muhammad Asif Zahoor
Alhothuali, Muhammed Shabab
Shoaib, Muhammad
author_facet Aljohani, Jawaher Lafi
Alaidarous, Eman Salem
Raja, Muhammad Asif Zahoor
Alhothuali, Muhammed Shabab
Shoaib, Muhammad
author_sort Aljohani, Jawaher Lafi
collection PubMed
description In the present study, modeling of intelligent numerical computing through Levenberg–Marquardt back propagation-based supervised neural network (LMB-SNN) is incorporated to analyze the magnetohydrodynamic flow of a third grade fluid for wire coating analysis (MHD-TGFWCA). The original mathematical formulations in terms of partial differential equations for MHD-TGFWCA are converted into a system of ordinary differential equations through dimensionless parameters and a suitable transformation mechanism. A reference dataset for the LMB-SNNs scheme is created with Adam’s numerical technique for various scenarios by variation of different physical quantities such as third grade fluid parameter, magnetic parameter, and the velocity ratio parameter. To compute the approximate solution for MHD-TGFWCA in terms of various scenarios, the training, testing, and validation operations are carried out in parallel to adjust neural networks by developing the mean square error function (MSEF) through Levenberg–Marquardt back-propagation. The comparative analyses and performance studies through outputs of MSEF, regression illustrations, and error histograms validate the effectiveness of the suggested solver LMB-SNNs. The method’s precision is verified by the closest numerical outputs of both built and dataset values with similar levels [Formula: see text] to [Formula: see text] .
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spelling pubmed-84795002021-09-29 Supervised Learning Algorithm to Study the Magnetohydrodynamic Flow of a Third Grade Fluid for the Analysis of Wire Coating Aljohani, Jawaher Lafi Alaidarous, Eman Salem Raja, Muhammad Asif Zahoor Alhothuali, Muhammed Shabab Shoaib, Muhammad Arab J Sci Eng Research Article-Physics In the present study, modeling of intelligent numerical computing through Levenberg–Marquardt back propagation-based supervised neural network (LMB-SNN) is incorporated to analyze the magnetohydrodynamic flow of a third grade fluid for wire coating analysis (MHD-TGFWCA). The original mathematical formulations in terms of partial differential equations for MHD-TGFWCA are converted into a system of ordinary differential equations through dimensionless parameters and a suitable transformation mechanism. A reference dataset for the LMB-SNNs scheme is created with Adam’s numerical technique for various scenarios by variation of different physical quantities such as third grade fluid parameter, magnetic parameter, and the velocity ratio parameter. To compute the approximate solution for MHD-TGFWCA in terms of various scenarios, the training, testing, and validation operations are carried out in parallel to adjust neural networks by developing the mean square error function (MSEF) through Levenberg–Marquardt back-propagation. The comparative analyses and performance studies through outputs of MSEF, regression illustrations, and error histograms validate the effectiveness of the suggested solver LMB-SNNs. The method’s precision is verified by the closest numerical outputs of both built and dataset values with similar levels [Formula: see text] to [Formula: see text] . Springer Berlin Heidelberg 2021-09-29 2022 /pmc/articles/PMC8479500/ /pubmed/34603928 http://dx.doi.org/10.1007/s13369-021-06212-3 Text en © King Fahd University of Petroleum & Minerals 2021 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Research Article-Physics
Aljohani, Jawaher Lafi
Alaidarous, Eman Salem
Raja, Muhammad Asif Zahoor
Alhothuali, Muhammed Shabab
Shoaib, Muhammad
Supervised Learning Algorithm to Study the Magnetohydrodynamic Flow of a Third Grade Fluid for the Analysis of Wire Coating
title Supervised Learning Algorithm to Study the Magnetohydrodynamic Flow of a Third Grade Fluid for the Analysis of Wire Coating
title_full Supervised Learning Algorithm to Study the Magnetohydrodynamic Flow of a Third Grade Fluid for the Analysis of Wire Coating
title_fullStr Supervised Learning Algorithm to Study the Magnetohydrodynamic Flow of a Third Grade Fluid for the Analysis of Wire Coating
title_full_unstemmed Supervised Learning Algorithm to Study the Magnetohydrodynamic Flow of a Third Grade Fluid for the Analysis of Wire Coating
title_short Supervised Learning Algorithm to Study the Magnetohydrodynamic Flow of a Third Grade Fluid for the Analysis of Wire Coating
title_sort supervised learning algorithm to study the magnetohydrodynamic flow of a third grade fluid for the analysis of wire coating
topic Research Article-Physics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8479500/
https://www.ncbi.nlm.nih.gov/pubmed/34603928
http://dx.doi.org/10.1007/s13369-021-06212-3
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