Further analysis of double-diffusive flow of nanofluid through a porous medium situated on an inclined plane: AI-based Levenberg–Marquardt scheme with backpropagated neural network

The present article exploits a novel application of AI-based Levenberg–Marquardt scheme with backpropagated neural network (LMS–BPNN) to analyze the double-diffusive free convection nanofluid flow model (DDFC-NFM) over an inclined plate in existence Brownian motion and thermophoresis properties embedded porous medium. governing PDEs representing DDFC-NFM are transformed into system nonlinear ODEs by applying suitable transformation. reference data set is generated from Lobatto III-A numerical solver variation magnetic field parameter (M), thermal Grashof number (Gr), angle inclination (?), (Nb), Dufour-solutal Lewis (Ld), modified Dufour (Nd) (Nt) for all scenarios designed LMS–BPNN. approximate solution its comparison standard analyzed execution training, testing validation procedure effectiveness reliable performance LMS–BPNN endorsed MSE-based fitness curve, regression analysis, error histogram analysis correlation index. Results reveal that velocity increases rise Gr, whereas reverse trend has been noticed temperature profile increase Nb, Nd Nt. solutal concentration increment Ld, while causes decrease it. When Nt increases, enhancement nanoparticle volume frictions occurs, but opposite behavior depicted parameter.