MHD Boundary Layer Flow over a Stretching Sheet: A New Stochastic Method

In this study, a new computing model is developed using the strength of feed-forward neural networks with Levenberg–Marquardt scheme-based backpropagation technique (NN-BLMS). It used to find solution for nonlinear system obtained from governing equations magnetohydrodyanmic (MHD) boundary layer flow over stretching sheet. Moreover, partial differential (PDEs) MHD sheet are converting into ordinary (ODEs) help similarity transformation. A dataset proposed NN-BLMM-based generated at different scenarios by variation various embedding parameters: Deborah number β and magnetic parameter (M). The training (TR), testing (TS), validation (VD) NN-BLMS evaluated in compare results reference results. For fluidic convergence analysis, metrics, such as mean square error (MSE), histogram (EH), regression (RG) plots, utilized measuring effectiveness performance infrastructure model. experiments showed that comparisons between match terms up E-02 E-10. This proves validity Furthermore, demonstrated there decrease thickness increasing parameter. importance experiment can be seen due its industrial applications power generation, generators, pumps.