https://doi.org/10.1140/epjp/s13360-023-04852-y
Regular Article
Supervised stochastic Levenberg–Marquardt intelligent netwoks for dynamics of convective Eyring–Powell magneto-nanofluid model
1
Department of Mathematics, COMSATS University Islamabad, Islamabad Campus, Pakistan
2
Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, 64002, Douliou, Yunlin, Taiwan, ROC
3
Yuan Ze University, AI Center, 320, Taoyuan, Taiwan
4
Department of Mathematics, Mohi-Ud-Din Islamic University, Nerian Sharif, AJK, Pakistan
Received:
5
July
2023
Accepted:
29
December
2023
Published online:
19
February
2024
The presented work examines the dynamics of convective Eyring–Powell magneto-nanofluid model (CEP-MNFM) with a stretching cylinder by using stupendous knacks of supervised stochastic Levenberg–Marquardt intelligent networks (SSLMINs). The partial differential equations governing the CEP-MNFM are reduced into coupled ODEs by incorporating the similarity transformations. The dataset of the proposed SSLMINs approach is generated with state-of-the-art Adam numerical method for seven different scenarios of CEP-MNFM including variation of radiation, Brownian diffusivity, and thermophoresis parameter, as well as, Biot, Schmidh, and Prandtl numbers. The reference dataset is further utilized for numerical calculation of various physical quantities on CEP-MNFM by applying the AI based methods via SSLMINs. The precision and accuracy of the designed SSLMINs approach is efficaciously substantiated through the negligible level of mean squared error with magnitude around 10–8 to 10–10, histograms with maximum instances error range 10–5, very near to the optimum correlation/regression measures.
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© The Author(s), under exclusive licence to Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.