#Physics-based Deep Learning

This book serves as a detailed guide to combining deep learning with physical simulations, featuring practical Jupyter notebooks. It focuses on solving PDE problems using deep learning while integrating existing physical knowledge and numerical methods. The latest version adds extensive content on differentiable physics training and innovative learning techniques. Highlights include hybrid fluid flow solvers, Bayesian Neural Networks for RANS flow with uncertainty predictions, and reinforcement learning for PDE control, making it a valuable asset for enhancing AI-driven physical modeling.