A machine-learning-assisted numerical study is conducted on inclined magnetohydrodynamic (MHD) micropolar bioconvective flow over a curved permeable stretching surface, incorporating non-Darcy porous resistance. The mathematical model combines thermal radiation, chemical reactions, mass diffusion, and motile microorganisms to represent coupled bio-magneto-thermal transport phenomena. Similarity transformations are used to reduce the governing nonlinear boundary-layer equations to a system of ordinary differential equations, which are solved numerically using MATLAB’s bvp4c solver. The resulting numerical dataset is used to train a feedforward Artificial Neural Network (ANN) with the Levenberg-Marquardt algorithm to predict key engineering parameters. Results show that increasing the magnetic parameter reduces velocity by approximately 9%, whereas non-Darcy porous resistance reduces fluid motion by 18%. Thermal radiation increases the temperature field by approximately 14%, whereas a higher Schmidt number reduces the concentration by nearly 73%. Similarly, higher Peclet numbers reduce the number of microorganisms by approximately 71%, illustrating the importance of advective transport. The ANN model shows high predictive accuracy, evidenced by a coefficient of determination close to 1 and low RMSE and MAE values. This combined numerical and machine-learning method provides an accurate and computationally efficient approach to studying complex inclined MHD micropolar bioconvective systems, which are important for advanced thermal and bioengineering applications.
Gangadri et al. (Wed,) studied this question.