Intelligent computing for magnetohydrodynamic micropolar nanofluid with stratification using Levenberg–Marquardt backpropagation algorithm

The magnetohydrodynamic (MHD) micropolar nanofluid with stratification is evaluated in this work by integrated numerical computing using the Levenberg Marquardt backpropagation (LMBB) optimization technique, an artificial neural network (ANN) approach. After that, model is condensed to a set of prob...

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Vydáno v:Journal of computational science Ročník 92; s. 102727
Hlavní autoři: Haq, Ikram Ul, Shukat, Saira, Ullah, Ikram, Hassan, Waqar Ul, Zhang, Hong-Na, Li, Xiao-Bin, Li, Feng-Chen
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 01.12.2025
Témata:
Artificial neural network
Double stratification, Suction/Injection
Levenberg-Marquardt
Machine learning
MHD
Nanofluid
Riga plate
Levenberg-Marquardt
Riga plate
Nanofluid
Double stratification, Suction/Injection
Machine learning
MHD
Artificial neural network
ISSN:1877-7503
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Shrnutí:The magnetohydrodynamic (MHD) micropolar nanofluid with stratification is evaluated in this work by integrated numerical computing using the Levenberg Marquardt backpropagation (LMBB) optimization technique, an artificial neural network (ANN) approach. After that, model is condensed to a set of problems with boundary values, which are resolved utilizing the proposed method LMBB algorithm and a numerical technique BVP4c. The LMBB approach is an iterative approach for figuring out the least of a function that is not linear, is distinct as the addition of squares. The outcomes are also cross-checked against those of earlier studies and the MATLAB’s BVP4c solver for validation. The mapping of velocity, concentration and temperature profiles from the input to results is another use of neural networking. These results show the accuracy level of the predictions and improvements made by ANN. To generalize a dataset, the BVP4c techniques’ performance is utilized to lower error of mean square. Data based on the ratio of training (80 %), validation (10 %) and testing (10 %) is used by the ANN-based LMBB backpropagation optimization technique. Histograms and function fitness are utilized to verify the algorithm’s dependability. For fluid dynamics, numerical methods and ANN perform incredibly well together, and this could result in new developments across a wide range of fields. The results of this study may aid in the optimization of fluid systems, leading to higher productivity and efficiency in a range of engineering applications.
ISSN:1877-7503
DOI:10.1016/j.jocs.2025.102727