Machine learning intelligent based hydromagnetic thermal transport under Soret and Dufour effects in convergent/divergent channels: a hybrid evolutionary numerical algorithm.

In this research, we analyze the complex dynamics of hydro-magnetic flow and heat transport under Sorent and Dofour effects within wedge-shaped converging and diverging channels emphasizing its critical role in conventional system design, high-performance thermal equipment. We utilized artificial neural networks (ANNs) to investigation the dynamics of the problem. Our study centers on unraveling the intricacies of energy transport and entropy production arising from the pressure-driven flow of a non-Newtonian fluid within both convergent and divergent channel. The weights of ANN based fitness function ranging from - 10 to 10. To optimize the weights and biases of artificial neural networks (ANNs), employ a hybridization of advanced evolutionary optimization algorithms, specifically the artificial bee colony (ABC) optimization integrated with neural network algorithms (NNA). This approach allows us to identify and fine-tune the optimal weights within the neural network, enabling accurate prediction. We compare our results against the established different analytical and numerical methods to assess the effectiveness of our approach. The methodology undergoes a rigorous evaluation, encompassing multiple independent runs to ensure the robustness and reliability of our findings. Additionally, we conduct a comprehensive analysis that includes metrics such as mean squared error, minimum values, maximum values, average values, and standard deviation over these multiple independent runs. The minimum fitness function value is 1.32 × 10