Neural network-based predictive model for heat transfer rate in magnetohydrodynamic flow over a stretching cylinder with Cattaneo–Christov heat flux | Synapse
March 3, 2026
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.