Hall effect on MHD Jeffrey fluid flow with Cattaneo–Christov heat flux model: an application of stochastic neural computing

Abstract Exploration and exploitation of intelligent computing infrastructures are becoming great interest for the research community to investigate different fields science engineering offering new improved versions problem-solving soft computing-based methodologies. The current investigation presents a novel artificial neural network-based solution methodology presented problem addressing properties Hall on magneto hydrodynamics (MHD) flow with Jeffery fluid towards nonlinear stretchable sheet thickness variation. Generalized heat flux characteristics employing Cattaneo–Christov model (CCHFM) along modified Ohms law have been studied. modelled PDEs reduced into dimensionless set ODEs by introducing appropriate transformations. temperature velocity profiles examined numerically help Adam Bashforth method values physical parameters study Jeffrey CCHFM. examination input–output network numerical results is also conducted obtained dataset system using Levenberg Marquardt backpropagated networks. value Skin friction coefficient, Reynold number, Deborah Nusselt local wall factors calculated interpreted better insight dynamics. precision level exhaustively mean square error, error histograms, training states information, regression fitting plots. Moreover, performance designed solver certified error-based learning curves, metrics histogram analysis. Several significant magnetic field in graphical tabular form.