Physics-based Deep Learning

Understand

This document is a hands-on, comprehensive guide to deep learning in the realm of physical simulations.

• Rather than just theory, we emphasize practical application: every concept is paired with interactive Jupyter notebooks to get you up and running quickly.
• Beyond traditional supervised learning, we dive into physical loss-constraints, differentiable simulations, diffusion-based approaches for probabilistic generative AI, as well as reinforcement learning and advanced neural network architectures.
• These foundations are paving the way for the next generation of scientific foundation models.