Physics-Informed Neural Networks for Inverse Modeling of Viscosity in Injection Molding
Physics-informed neural networks integrate physical laws into machine learning models to solve differential equations. This thesis investigates an inverse physics-informed neural network framework for estimating viscosity in Tetra Pak’s injection molding process, where direct measurements are not possible. By enforcing the Navier-Stokes equations and using sparse velocity and pressure data, a virt