Artificial neural network paradigm of magneto-thermal behavior in tangent hyperbolic hybrid-nanofluid flow

The study considers the flow behavior of a Magnetohydrodynamic (MHD) tangent-hyperbolic hybrid-nanofluid as it flows on an exponentially stretched surface. Boundary slippage, Joule heating, changes in thermal radiation and convective effects have impacts on thermal exchange rate. The fluid used for these experiments is ethylene glycol (EG) and copper with alumina $$(Cu + Al_{2} O_{3} ) \,$$ nanoparticles to enhance heat transfer. The dictating partial differential equations (PDEs) transformed into ordinary differential equations (ODEs) employing suitable similarity techniques. Artificial Intelligence (AI) based Machine Learning (ML) Levenberg Marquardt Algorithm (LMA) is used to determine the impact of parameters involved in MHD tangent hyperbolic nanofluid flow. The influence of flow rate and thermal heat transfer are studied through graphical analyses. A rise in magnetic parameter and ratio of elastic to viscous drags causes decline in flow. While the Lorentz parameter and ambient temperature difference boosts the temperature profile on rising. The ANN-LMA had mean-squared-error in the range of 7.62 × 10–11 to 4.59 × 10–10 on the six cases and also converged within 79–1000 epochs based on the experiment and good regression fits were displayed. This research is useful in paper production, cooling metal sheets, and crystal growth. The novelty lies in applying LMA-based ANN to approximate the model which has not been reported previously.