Use of wavelet-based neural networks for optimization of heat and mass transfer radiative hybrid nanofluid over a nonlinear stretching surface

Scientists are primarily interested in artificial neural networks because of their wide range of modeling and analytical applications. Data networks are often developed using artificial neural networks, one of the taught learning algorithms, which optimizes the error between real and predicted values. Because they can handle enormous information and represent complicated systems, artificial neural networks have a wide range of applications across many areas. The objective of the present work is to investigates the continuous, incompressible hybrid nanofluid motion through a nonlinear extending sheet with the impact of the magnetic field, activation energy, heat radiation, Maxwell velocity slip, and Smoluchowski thermal slip. The Runge-Kutta-Fehlberg 4th 5th order and shooting technique are employed to solve the governing equations once they have been transformed into ordinary differential equations, utilizing the proper similarity variables. The consequence of non-dimensional parameters concerning their profiles is examined using graphs. And the primary results are, the velocity profile falls as the Maxwell velocity slip parameter rises. As the Smoluchowski temperature parameter increases, the temperature profile will drop as the radiation parameter enhances, the temperature profile rises as well, as the reaction rate parameter increases, the concentration falls, and the concentration profile rises as the activation energy parameter is improved. The actual and predicted data for the power index, magnetic field parameter, activation energy parameter, Radiation parameter, and reaction rate for all ranges of values are similar when applying a wavelet neural network.