Non-Newtonian Mixed Convection Magnetized Flow with Heat Generation and Viscous Dissipation Effects: A Prediction Application of Artificial Intelligence

A non-Newtonian stagnation point fluid flow towards two different inclined heated surfaces is mathematically formulated with pertinent effects, namely mixed convection, viscous dissipation, thermal radiations, heat generation, and temperature-dependent conductivity. Mass transfer additionally considered by the use of a concentration equation. The narrating equations are solved numerically using shooting method along Runge–Kutta scheme. total 80 samples for five inputs, velocities ratio parameter, temperature Grashof number, Casson solutal magnetic field parameter. 70% data used training network; 15% validation; testing. skin friction coefficient (SFC) targeted output. Ten neurons in hidden layer. artificial networking models trained Levenberg–Marquardt algorithm. SFC values predicted cylindrical flat developed neural (ANN) models. shows decline velocity number. In an absolute sense, owning to prediction ANN models, we have seen that high magnitude case surface comparison surface. present results will serve as helpful source future studies on quantities intelligence.