Neural network-based predictive model for heat transfer rate in magnetohydrodynamic flow over a stretching cylinder with Cattaneo–Christov heat flux

Key Points

• The predictive model demonstrates improved accuracy in estimating heat transfer rates under various conditions.
• Key evidence shows enhancement in prediction reliability, with a correlation coefficient reaching 0.95 in some scenarios.
• Observational analysis of the Cattaneo–Christov heat flux model employed neural networks for advanced predictions of thermal dynamics.
• The results support the advancement of predictive modeling, indicating its relevance for engineering applications in fluid dynamics.