Design of inverse multiquadric radial basis neural networks for the dynamical analysis of MHD casson nanofluid flow along a nonlinear stretchable porous surface with multiple slip conditions.

The present research, a numerical approach to examine magnetohydrodynamics (MHD) Casson nanofluid flow in a porous medium along a stretchable surface with different slips using artificial neural networks (ANNs) by taking inverse multiquadric (IMQ) radial basis function (RBF) as an activation function. i.e. ANNs-IMQ-RBF. The hybridization of genetic algorithms (GAs) and sequential quadratic programming (SQP) is used for learning in ANNs-IMQ-RBF. The PDEs representing the fluid flow are converted into a nonlinear system of dimensionless form of ODEs through an appropriate transformation while effects of variation in the values of Casson parameter (β), Brownian motion parameter (Nb), Prandtl number (Pr), stretching parameter (n), porosity parameter (P), Lewis number (Le) along with temperature slip parameter (λ 2) on velocity, temperature and nanofluid concentration are depicted through graphs. The effectiveness, convergence and accuracy of the proposed solver are validated evidently through boxplot analysis, histograms and cumulative distribution function (CDF) plots. [Display omitted] • A new ANNs-IMQ-RBF solver is designed for MHD Casson nanofluid problem in the shape of a system of nonlinear ODEs. • Velocity, temperature and concentration profiles are calculated for sundry cases of physical parameters of the interest. • Results are compared with the reference solutions of Adams numerical technique to analyze the accuracy and convergence. • Statistical operators via box plot, CDF and histograms analyses are used to verify the effectiveness and stability. [ABSTRACT FROM AUTHOR]