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A Novel Stochastic Framework for the MHD Generator in Ocean

Sakda Noinang1, Zulqurnain Sabir2, Shumaila Javeed3, Muhammad Asif Zahoor Raja4, Dostdar Ali3, Wajaree Weera5,*, Thongchai Botmart5

1 Department of Mathematics Statistics and Computer, Faculty of Science, Ubon Ratchathani University, Ubon Ratchathani 34190, Thailand
2 Department of Mathematics and Statistics, Hazara University, Mansehra, Pakistan
3 Department of Mathematics, Comsats University Islamabad, 45550 Islamabad Campus, Islamabad, Pakistan
4 Future Technology Research Center, National Yunlin University of Science and Technology, Yunlin 64002, Taiwan
5 Department of Mathematics, Faculty of Science, Khon Kaen University, Khon Kaen 40002, Thailand

* Corresponding Author: Wajaree Weera. Email: email

Computers, Materials & Continua 2022, 73(2), 3383-3402. https://doi.org/10.32604/cmc.2022.029166

Abstract

This work aims to study the nonlinear ordinary differential equations (ODEs) system of magnetohydrodynamic (MHD) past over an inclined plate using Levenberg-Marquardt backpropagation neural networks (LMBNNs). The stochastic procedures LMBNNs are provided with three categories of sample statistics, testing, training, and verification. The nonlinear MHD system past over an inclined plate is divided into three profiles, dimensionless momentum, species (salinity), and energy (heat) conservations. The data is applied 15%, 10%, and 75% for validation, testing, and training to solve the nonlinear system of MHD past over an inclined plate. A reference data set is designed to compare the obtained and proposed solutions for the MHD system. The plots of the absolute error (AE) are provided to check the accuracy and precision of the considered nonlinear system of MHD. The obtained numerical solutions of the nonlinear magnetohydrodynamic system have been considered to reduce the mean square error (MSE). For the capability, dependability, and aptitude of the stochastic LMBNNs procedure, the numerical performances are provided to authenticate the relative arrangements of MSE, error histograms (EHs), state transitions (STs), correlation, and regression.

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Cite This Article

APA Style
Noinang, S., Sabir, Z., Javeed, S., Raja, M.A.Z., Ali, D. et al. (2022). A novel stochastic framework for the MHD generator in ocean. Computers, Materials & Continua, 73(2), 3383-3402. https://doi.org/10.32604/cmc.2022.029166
Vancouver Style
Noinang S, Sabir Z, Javeed S, Raja MAZ, Ali D, Weera W, et al. A novel stochastic framework for the MHD generator in ocean. Comput Mater Contin. 2022;73(2):3383-3402 https://doi.org/10.32604/cmc.2022.029166
IEEE Style
S. Noinang et al., "A Novel Stochastic Framework for the MHD Generator in Ocean," Comput. Mater. Contin., vol. 73, no. 2, pp. 3383-3402. 2022. https://doi.org/10.32604/cmc.2022.029166



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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