Advanced Machine Learning Approach for Casson fluid Flow with Thermal Radiation and Viscous Dissipation Effects

The growing interest in artificial intelligence applications has led researchers to explore soft computing techniques for their ability to provide thorough analysis, precise modeling, and efficient simulations. The objective of this study is to apply Levenberg-Marquardt algorithm in solving complex magnetized boundary layer flow in three-dimensional Radiative Casson Flow with Thermo-Diffusion and Chemical Reactions. Further, Casson model is formulated at a three-dimensional surface with Cattaneo-Christov heat flux, Diffusion-thermo, Joule heating effects. By introducing suitable similarity variables, ordinary differential equations are derived from partial differential equations. To compute the mass and heat transfer coefficient at three-dimensional surfaces, the BVP4C scheme is used. In order to make comparisons between mass and thermal flow regimes, various flow regimes are considered. We have used the Levenberg–Marquardt training technique to construct artificial neural networks for more detailed analysis. A dataset is generated using the bvp4c solver for different Levenberg-Marquardt scenarios, facilitating robust analysis through mean squared error, state transition, error histograms, and regression analysis. The proposed artificial neural frameworks offer an intelligent computational strategy, ensuring highly accurate numerical approximations. The effectiveness and reliability of this solver are demonstrated through numerical validation and graphical representation, paving the way for efficient AI-driven solutions in nonlinear transport phenomena. Thermal relaxation and concentration parameters regulate the mass, thermal and diffusion, as well as thickness of the layers related to them. It is imperative that viscous dissipation as well as thermal radiation play a significant role in obtaining the maximum amount of thermal energy. Chemical industry applications benefit from the ability to model non-Newtonian fluids and improve their mass transfer properties.