Intelligent adaptive nonlinear autoregressive eXogeneous neuro‐structure for ferromagnetic Powell‐Eyring fluidic involving cubic autocatalysis chemical reaction

Abstract The scope of artificial intelligence in the field of fluid mechanics has been expanded with the development sophisticated technology to enhance the efficiency, reliability, solve complexities, introduced alternate transformation and enabling more dependable solutions with their analysis. The goal of this study is to investigate the ferromagnetic Powell‐Eyring fluids (FMPEFs) model with non‐Fourier heat flux by using artificial intelligence‐based scheme by exploiting the adaptive nonlinear autoregressive eXogenous (NARX) neuro‐architecture with backpropagation of Levenberg Marquart (LM), that is, NARX‐LM. The developed NARX‐LM methodology applied on synthetic datasets acquired with the help of Adams numerical method for FMPEF system by prudently changing physical quantities that is, material parameters of Eyring Powell, homogeneous reaction, heterogeneous reaction, dimensionless thermal relaxation time, Prandtl number, Schmidt number with fixed values parameter of ferrohydrodynamic interaction, rate of diffusion coefficient. Outcomes of NARX‐LM are regularly overlapping with the numerical results for the FMPEFs system having reasonable small error magnitude for each variant. The proficiency of intelligent computing anticipated on FMPEFs is depicted exhaustively with iterative mean squared error based iconvergence curves, analysis of adaptive controlling factors, error frequency distribution on the histograms, auto‐correlation, and correlation measures.