A comprehensive review on artificial neural network techniques (Levenberg–Marquardt, Bayesian regularization, scaled conjugate gradient) for magnetohydrodynamic hybrid nanofluid flow with bio-convection and heat sources

Integration of artificial intelligence into computational fluid dynamics has significantly enhanced the simulation of complex transport phenomena. This review presents a detailed analysis of artificial neural network (ANN) techniques, namely Levenberg Marquardt (LM), Bayesian Regularization (BR), and Scaled Conjugate Gradient (SCG), in the simulation of magnetohydrodynamic (MHD) hybrid nanofluid flows with bio-convection and internal heat generation. Such flows are characterized by strong nonlinearities arising from the synergistic interaction of magnetic forces, nanoparticle-particle interactions, convective effects caused by microorganisms, and heat gradients. Traditional numerical methods, though well-refined, typically face convergence issues, computational costs, and adaptability to highly nonlinear cases. ANN-based models, on the other hand, show remarkable characteristics toward the approximation of intricate physical correlations with enhanced convergence and generalization. This review discusses, in a detailed manner, the theoretical basis, training behavior, prediction accuracy, and computational efficiency of LM, BR, and SCG algorithms in maintaining critical fluid properties like velocity, temperature, and concentration. Special focus is given to the role played by bio-convection in enhancing transport properties and to how ANN techniques effectively model these dynamics with minimal residual error and computational intensity. A comparative study highlights the applicability and uniformity of such neural network algorithms in advanced heat and mass transfer operations with MHD hybrid nanofluids.