A machine learning approach to the prediction of transport and thermodynamic processes in multiphysics systems - heat transfer in a hybrid nanofluid flow in porous media

•An artificial intelligence based predictive models for estimations of transport and thermodynamics processes.•This study dealt with a hybrid nanofluid SPF past a cylinder embedded in porous media.•The computational results were then used to train an artificial intelligence tool developed through supervised learning methods.•The validated predictive tool was subsequently used to estimate the behaviours of the temperature fields, Nusselt and Bejan number and, shear stress over the cylinder.