Neural network-driven analysis of magnetized dissipative Ree-Eyring fluid flow with Cattaneo-Christov heat flux on a permeable surface

This work investigates the magnetohydrodynamic flow of a dissipative Ree-Eyring fluid over a permeable stretching surface, subjected to an inclined magnetic field relative to the direction of fluid motion. Thermal transport is analyzed using the Cattaneo-Christov heat flux model, which accounts for thermal relaxation effects absent in the classical Fourier formulation. The flow regime further incorporates the influence of a Darcy-Forchheimer porous medium, introducing both linear and quadratic drag contributions to the momentum equation. The main equations have initially evaluated numerically through bvp4c approach in dimensionless form. The dataset generated through the bvp4c numerical scheme is subsequently employed to implement the artificial neural network (ANN) methodology. It has revealed as outcomes of this work that optimal convergence achieved through ANN approach at epochs 134, 205, and 275 across the three scenarios. Error histograms and fitness evaluation confirm solution stability, progressive improvement, and close alignment between predicted and expected values. With growth in Weissenberg number [Formula: see text], magnetic parameter [Formula: see text], Darcy Forchheimer factor [Formula: see text] and porosity parameter [Formula: see text] there is decline in velocity [Formula: see text]. Thermal distribution [Formula: see text] augmented with growth in radiation parameter [Formula: see text], Brownian motion parameter [Formula: see text], thermo-phoresis parameter [Formula: see text], heat source/sink factor [Formula: see text] while declined with higher Prandtl number [Formula: see text] and thermal relaxation time parameter [Formula: see text].