Intelligent computing for electromagnetohydrodynamic bioconvection flow of micropolar nanofluid with thermal radiation and stratification: Levenberg–Marquardt backpropagation algorithm
The Levenberg–Marquardt (LM) backpropagation optimization algorithm, an artificial neural network algorithm, is used in this study to perform integrated numerical computing to evaluate the electromagnetohydrodynamic bioconvection flow of micropolar nanofluid with thermal radiation and stratification...
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| Vydáno v: | AIP advances Ročník 14; číslo 3; s. 035224 - 035224-25 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Melville
American Institute of Physics
01.03.2024
AIP Publishing LLC |
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| ISSN: | 2158-3226, 2158-3226 |
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| Abstract | The Levenberg–Marquardt (LM) backpropagation optimization algorithm, an artificial neural network algorithm, is used in this study to perform integrated numerical computing to evaluate the electromagnetohydrodynamic bioconvection flow of micropolar nanofluid with thermal radiation and stratification. The model is then reduced to a collection of boundary value problems, which are solved with the help of a numerical technique and the proposed scheme, i.e., the LM algorithm, which is an iterative approach to determine the minimum of a nonlinear function defined as the sum of squares. As a blend of the steepest descent and the Gauss–Newton method, it has become a typical approach for nonlinear least-squares problems. Furthermore, the stability and consistency of the algorithm are ensured. For validation purposes, the results are also compared with those of previous research and the MATLAB bvp4c solver. Neural networking is also utilized for velocity, temperature, and concentration profile mapping from input to output. These findings demonstrate the accuracy of forecasts and optimizations produced by artificial neural networks. The performance of the bvp4c solver, which is used to reduce the mean square error, is used to generalize a dataset. The artificial neural network-based LM backpropagation optimization algorithm operates using data based on the ratio of testing (13%), validation (17%), and training (70%). This stochastic computing work presents an activation log-sigmoid function based LM backpropagation optimization algorithm, in which tens of neurons and hidden and output layers are used for solving the learning language model. The overlapping of the results and the small computed absolute errors, which range from 10−3 to 10−10 and from 106 to 108 for each model class, indicate the accuracy of the artificial neural network-based LM backpropagation optimization algorithm. Furthermore, each model case’s regression performance is evaluated as if it were an ideal model. In addition, function fitness and histogram are used to validate the dependability of the algorithm. Numerical approaches and artificial neural networks are an excellent combination for fluid dynamics, and this could lead to new advancements in many domains. The findings of this research could contribute to the optimization of fluid systems, resulting in increased efficiency and production across various technical domains. |
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| AbstractList | The Levenberg–Marquardt (LM) backpropagation optimization algorithm, an artificial neural network algorithm, is used in this study to perform integrated numerical computing to evaluate the electromagnetohydrodynamic bioconvection flow of micropolar nanofluid with thermal radiation and stratification. The model is then reduced to a collection of boundary value problems, which are solved with the help of a numerical technique and the proposed scheme, i.e., the LM algorithm, which is an iterative approach to determine the minimum of a nonlinear function defined as the sum of squares. As a blend of the steepest descent and the Gauss–Newton method, it has become a typical approach for nonlinear least-squares problems. Furthermore, the stability and consistency of the algorithm are ensured. For validation purposes, the results are also compared with those of previous research and the MATLAB bvp4c solver. Neural networking is also utilized for velocity, temperature, and concentration profile mapping from input to output. These findings demonstrate the accuracy of forecasts and optimizations produced by artificial neural networks. The performance of the bvp4c solver, which is used to reduce the mean square error, is used to generalize a dataset. The artificial neural network-based LM backpropagation optimization algorithm operates using data based on the ratio of testing (13%), validation (17%), and training (70%). This stochastic computing work presents an activation log-sigmoid function based LM backpropagation optimization algorithm, in which tens of neurons and hidden and output layers are used for solving the learning language model. The overlapping of the results and the small computed absolute errors, which range from 10−3 to 10−10 and from 106 to 108 for each model class, indicate the accuracy of the artificial neural network-based LM backpropagation optimization algorithm. Furthermore, each model case’s regression performance is evaluated as if it were an ideal model. In addition, function fitness and histogram are used to validate the dependability of the algorithm. Numerical approaches and artificial neural networks are an excellent combination for fluid dynamics, and this could lead to new advancements in many domains. The findings of this research could contribute to the optimization of fluid systems, resulting in increased efficiency and production across various technical domains. |
| Author | Ali, Aatif Khan, Zeeshan Alfwzan, Wafa F. Asamoah, Joshua Kiddy K. Innab, Nisreen Zuhra, Samina Islam, Saeed |
| Author_xml | – sequence: 1 givenname: Zeeshan surname: Khan fullname: Khan, Zeeshan organization: Department of Mathematics, Abdul Wali Khan University Mardan – sequence: 2 givenname: Wafa F. surname: Alfwzan fullname: Alfwzan, Wafa F. organization: Department of Mathematical Sciences, College of Science, Princess Nourah Bint Abdulrahman University – sequence: 3 givenname: Aatif surname: Ali fullname: Ali, Aatif organization: 7Kwame Nkrumah University of Science and Technology, Kumasi, Ghana – sequence: 4 givenname: Nisreen surname: Innab fullname: Innab, Nisreen organization: Department of Computer Science and Information Systems, College of Applied Sciences, AlMaarefa University – sequence: 5 givenname: Samina surname: Zuhra fullname: Zuhra, Samina organization: Department of Computing and Technology, Abasyn University – sequence: 6 givenname: Saeed surname: Islam fullname: Islam, Saeed organization: Department of Mathematics, Abdul Wali Khan University Mardan – sequence: 7 givenname: Joshua Kiddy K. surname: Asamoah fullname: Asamoah, Joshua Kiddy K. organization: 7Kwame Nkrumah University of Science and Technology, Kumasi, Ghana |
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| Title | Intelligent computing for electromagnetohydrodynamic bioconvection flow of micropolar nanofluid with thermal radiation and stratification: Levenberg–Marquardt backpropagation algorithm |
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