This study investigates the steady, two-dimensional flow of Sisko fluid with a chemical reaction over an exponential sheet, employing suitable similarity variables to transform the governing partial differential equations into ordinary differential equations. The research is carried out in three phases: first, acquisition of the numerical data by the employment of the BVP4C solver; then, Response surface methodology (RSM) analysis serves to analyze the impact of the different parameters on the coefficient of skin friction, identifying which ones have the most influence. Finally, an artificial neural network (ANN) is employed to predict the numerically obtained skin friction coefficient and assess model accuracy. The effects of heat generation and chemical reaction are incorporated in the numerical formulation and reflected indirectly in the data used for ANN and RSM modeling. The response surface methodology (RSM) model, based on a central composite design (CCD), identified the porosity parameter ( K p ) as the most influential factor on the skin friction coefficient (effect size = 0.72), followed by N (0.18) and A (0.10). Statistical analysis confirmed the model’s reliability with a strong correlation (R² = 0.9944) and a non-significant lack-of-fit (p > 0.05). The ANN model achieved superior predictive accuracy, yielding a minimal mean squared error (MSE = 1.23 × 10⁻⁹) and high correlation (R² = 0.9960) during validation using a 70%–15%–15% data split for training, validation, and testing. Comparative results show that both RSM and ANN effectively capture nonlinear parameter interactions, with ANN demonstrating slightly better generalization. These findings highlight the robustness and predictive capability of the proposed hybrid RSM–ANN framework for predicting skin friction behavior in non-Newtonian Sisko fluid flow.
Fatima et al. (2026) studied this question.