A stochastic numerical analysis based on Levenberg–Marquardt backpropagation neural networks for entropy generation in nanofluid flow with radiation aspects

This paper delves into the realm of stochastic artificial intelligence AI-assisted computation methodology to analyze the mathematical model of nanofluid over stretchable sheet. Copper oxide [Formula: see text], Titanium Oxide [Formula: see text], and Carbon Nanotubes are used as nanoparticles with Sodium Alginate–Water solution [Formula: see text] as a base solvent. This research is of significant importance for energy-storing devices, electrical vehicles, biochemical engineering, pharmaceuticals, and many others. Governing the system of Partial Differential Equations (PDEs) is transformed into a system of ODEs with similarity parameters. Adam’s method is used for numerical dataset generation with the Mathematica tool. The stochastic technique of Levenberg–Marquardt Neural Network Feed-forward Algorithm (LMNNFA) is utilized for optimized evaluation. MATLAB assisted with further filtration and evaluations through the dataset generated. The intricate interplay of various thermophysical factors is studied. Flow rate, temperature and entropy profiles are presented against variations in involved parameters. It is observed that the fluid mobility reduces with inclination in Lorentz force due to electromagnetic effect and same behavior is observed with increasing porosity of surface, slip characteristic, and Casson parameter. Profiles of temperature show decline with inclination in mass transfer but get boosted with enhanced difference of sliding and static drag. Entropy generation presented a climb with an enhanced value of the slip effect and Casson effect. A clear agreement is observed with previous research. There is a need to use advancing technology to investigate the effects of thermophysical parameters of Tri-nano Hybrid Fluid (THF) composed of Carbon Nanotubes, Copper, and Titanium Oxides nanoparticles based on Sodium Alginate (CNCT-SA) under a variety of situations. Magnetohydrodynamic (MHD) flow of THF over stretchable sheet is important due to the broad spectrum of applications. The contents of this research are novel and unique.