Machine learning-based numerical study of radiative MHD hyperbolic tangent nanofluid flow over a stretching sheet
摘要
This article investigates the effect of the radiation parameter on MHD tangent hyperbolic nanofluid flow over a nonlinear stretching sheet. The governing PDEs are reduced to coupled nonlinear ODEs via similarity transformations, followed by the bvp4c solver in MATLAB. The results of earlier studies have shown that velocity boundary layer thickness decreases with increasing magnetic and Weissenberg numbers. In contrast, with increasing radiation and Brownian motion, the thickness of the thermal boundary layer increases. Using these numerical results, an Artificial Neural Network (ANN) is trained to improve computing performance and generate generalised predictions. The Levenberg–Marquardt (trainlm) technique is used in the ANN’s feed-forward backpropagation model to predict velocity, temperature, and concentration profiles based on eight input parameters. The main measures used to assess training success are mean squared error (MSE), gradient, validation failure (val fail), and adaptive learning rate (mu). Convergence is confirmed when the gradient drops dramatically during training from high starting values to as low as