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• W3159178679 abstract "In the current study, a modern implementation of intelligent numerical computational solver introduced using the Levenberg Marquardt algorithm based trained neural networks (LMA-TNN) to analyze the wire coating system (WCS) for the elastic-viscous non-Newtonian Eyring-Powell fluid (EPF) with the impacts of Joule heating, magnetic parameter and heat transfer scenarios in the permeable medium. The nonlinear PDEs describing the WCS-EPF are converted into dimensionless nonlinear ODEs containing the heat and viscosity parameters. The reference data for the designed LMA-TNN is produced for various scenarios of WCS-EPF representing with porosity parameter, non-Newtonian parameter, heat transfer parameter and magnetic parameter for the proposed analysis using the state of the art explicit Runge-Kutta technique. The training, validation, and testing operations of LMA-TNN are carried out to obtain the numerical solution of WCS-EPF for various cases and their comparison with the approximate outcomes certifying the reasonable accuracy and precision of LMA-TNN approach. The outcomes of LMA-TNN solver in terms of state transition (ST) index, error-histograms (EH) illustration, mean square error, and regression (R) studies further established the worth for stochastic numerical solution of the WCS-EPF. The strong correlation between the suggested and the reference outcomes indicates the structure's validity, for all four cases of WCS-EPF, fitting of the precision [Formula: see text] to [Formula: see text] is also accomplished." @default.
• W3159178679 created "2021-05-10" @default.
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• W3159178679 date "2021-04-27" @default.
• W3159178679 modified "2023-10-05" @default.
• W3159178679 title "Intelligent computing through neural networks for numerical treatment of non-Newtonian wire coating analysis model" @default.
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• W3159178679 doi "https://doi.org/10.1038/s41598-021-88499-8" @default.
• W3159178679 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/8079422" @default.
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