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This article addressed entropy generation of Nonlinear Electro-Magneto Mixed Convective nanofluid model (NEM-MCNM) with second-order velocity slip flow over a stretched surface via artificial backpropagated neural networks with Levenberg–Marquardt scheme (ABPNN-LMS). The ODEs are formed by applying suitable similarity transformation on system of PDEs for NEM-MCNM. The reference dataset is obtained by the variation of different parameters from homotopy analysis method (HAM) for all scenarios of proposed ABPNN-LMS. The reference data samples for training, testing and validation sets are used to find the approximate solution of proposed ABPNN-LMS and to compare it with reference HAM outcomes. The worthy and consistent performance of ABPNN-LMS is certified through MSE convergence, and the error histogram and regression index in terms of ABPNN-LMS are also analyzed. The impact of all variants on velocity, thermal gradient, concentration, skin friction, Bejan number, entropy generation, Sherwood and Nusselt number is discussed by conducting graphics and numerical studies. Results reveal that the decrease in motion of the fluid is seen for higher slip parameters. The temperature acts as an increasing function of Eckert number and thermophoresis parameter. The augmentation in activation energy enhances the concentration of the fluid, whereas reverse trend is noticed for reaction parameter. The absolute error results are obtained as 10–07–10–03 and 10–07–10–04 for velocity, 10–07–10–04 and 10–07–10–03 for temperature and 10–09–10–03 and 10–06–10–03 which determine the accuracy of ABPNN-LMS. The best performance is accomplished at, 265, 250, 636, 346, 299 and 182 epochs with MSE around E - 09 , E - 10 , E - 09 , E - 09 , E - 09 and E - 08 . [ABSTRACT FROM AUTHOR]

This article examines entropy production (EP) of magneto-hydrodynamics viscous fluid flow model (MHD-VFFM) subject to a variable thickness surface with heat sink/source effect by utilizing the intelligent computing paradigm via artificial Levenberg–Marquardt back propagated neural networks (ALM-BPNNs). The governing partial differential equations (PDEs) of MHD-VFFM are transformed into ODEs by applying suitable similarity transformations. The reference dataset is obtained from Adam numerical solver by the variation of Hartmann number (Ha), thickness parameter (α) , power index (n) , thermophoresis parameter (Nt), Brinkman number (Br), Lewis number (Le) and Brownian diffusion parameter (Nb) for all scenarios of proposed ALM-BPNN. The reference data samples arbitrary selected for training/testing/validation are used to find and analyze the approximated solutions of proposed ALM-BPNNs as well as comparison with reference results. The excellent performance of ALM-BPNN is consistently endorsed by Mean Squared Error (MSE) convergence curves, regression index and error histogram analysis. Intelligent computing based investigation suggests that the rise in values of Ha declines the velocity of the fluid motion but converse trend is seen for growing values of n. The rising values of Ha, Nt and Br improve the heat transfer but converse trend is seen for growing values of α. The inclining values of Nt incline the mass transfer but it shows reverse behavior for escalating values of Le. The inclining values of Br incline the EP. [ABSTRACT FROM AUTHOR]