Overview
An independent scientific-ML study: a nine-layer neural network trained with Levenberg-Marquardt optimization models hybrid nanofluid boundary-layer flow and heat transfer over a stretching sheet. Training data (about 32,400 samples) is generated from the physics equations themselves, so no experimental dataset dependency exists. The workflow runs end to end from one command (python src/main.py all: clean, generate, train with real-time visualization, save model and scalers in 8-22 minutes), followed by a separate validation stage computing MSE, RMSE, MAE, and R² against numerical solutions with publication-quality comparison plots. Implementation documentation ships in three formats.