Thermal Performance Analysis in Chemically Reactive Flow of Maxwell Fluid Using Physics‐Informed Neural Networks

The growing need for effective thermal management in chemical reactors, heat exchangers, cooling systems, and energy devices requires precise modeling of coupled heat and mass transfer in non‐Newtonian fluids under complex physicochemical circumstances. The current study examines the magnetohydrodynamic flow of a non‐Newtonian fluid in the presence of chemical reaction, activation energy, and heat generation/absorption at zero mass flux. The study combines Lie point symmetry analysis, the Runge–Kutta–Fehlberg technique, physics‐informed neural networks (PINNs) and Taguchi L16 optimization into a unified framework to evaluate and optimize heat and mass transport. Similarity transformations are used to convert the governing partial differential equations into nonlinear ordinary differential equations, which are then numerically solved. PINNs are used to increase the accuracy of flow, temperature, and concentration field predictions. The findings suggest that raising the Deborah number improves heat transfer under exothermic circumstances, but stronger chemical reactions improve mass transport while decreasing thermal transport. The activation energy considerably raises the concentration distribution. According to Taguchi analysis, the magnetic parameter has the least impact on temperature variation (9.880097%), whereas activation energy has the greatest influence (49.74787%). The suggested framework offers an effective technique for optimizing advanced thermal systems that operate under nonlinear transport circumstances.