This repository serves as an educational hub for Physics-Based Deep Learning, documenting methods that integrate neural networks with physical modeling. It catalogs approaches ranging from data-driven methods using physical data to differentiable simulators where deep learning is interleaved with physics simulations. The collection covers both forward simulations (predicting state or temporal evolution) and inverse problems (deriving physical parameters from observations), serving as a reference implementation and guide for researchers in scientific machine learning.