Thermal modeling of Johnson–Segalman nanofluid in blade coating process: a comparative study with machine learning framework

Abstract This study simulates the blade coating process of Johnson–Segalman (JS) nanofluid using machine learning, accounting for magnetic field, thermophoresis, and Brownian motion effects. Expressions are simplified by lubrication approximation theory (LAT) and numerically solved by shooting technique. A supervised neural network employing the Levenberg–Marquardt backpropagation (LMBP-SNN) algorithm was trained, tested, and validated using these numerical solutions by using regression plots an