We’re happy to publish v0.2 of our “Physics-Based Deep Learning” book #PBDL. The main goal is still a thorough hands-on introduction for physics simulations with deep learning, and the new version contains a large new part on improved learning methods. The main document is available at https://www.physicsbaseddeeplearning.org/ , or as PDF at https://arxiv.org/abs/2109.05237.
Among others, we’re explaining the “scale-invariant physics” training which includes higher-order information via inverse simulators. In naturally comes with code examples, e.g., this inverse heat problem:
Also, half-inverse gradients are explained in detail now. They jointly invert physics and neural network to get optimal updates for a whole mini-batch, here’s an example implementation that runs on the spot in colab:
… and of course we fixed numerous typos throughout all chapters, cleaned up the code, and clarified explanations. Please let us know if you find any 🙂 ! This text will also serve as the basis and script for our upcoming Advanced Deep Learning for Physics (IN2298) or shortened ADL4Physics course.
