This repository hosts an interactive book on physics-based deep learning, emphasizing practical applications over pure theory. Every concept is paired with executable Jupyter notebooks demonstrating deep learning for physical simulations. The content covers traditional supervised learning, physics-informed loss constraints, differentiable simulations, diffusion-based probabilistic methods for generative AI, reinforcement learning with simulators, and advanced neural network architectures for scientific computing.