A dual-head ANN architecture for multi-objective thermal optimization of MHD nanofluid flow within a wavy enclosure

Abstract This study examines magnetohydrodynamic natural convection of a Cu–H2O nanofluid inside a wavy undulating cavity and develops an artificial neural network (ANN)-based dual-output framework for predictive heat-transfer modelling and regime classification. To analyse the coupled impacts of Rayleigh number, nanoparticle volume fraction and wave number on flow motion and convection heat in the present work. A finite element method (FEM)-based numerical simulation is used to generate the dataset. An ANN is trained to predict the average Nusselt number along the flat plate from the dataset, and then classification of thermal regimes is performed into Low, Medium and High types using a quantile-based discretization method. The regression ANN has a high predictive accuracy, showing good correlation between the values of the simulated mean Nusselt number and R2 = 0.9878. The residuals of the model are well-distributed, and its generalization to test samples is stable. Moreover, the classification ANN also achieves good regime identification with accuracy (0.97%), supported by consistent fivefold cross-validation results and clear inter-class separation in latent-space feature projections. Overall, the integration of CFD-based simulation with ANN-driven regression and classification provides a computationally efficient surrogate modelling framework for rapid heat-transfer estimation and intelligent thermal regime mapping in complex enclosure geometries.