Updated readme

README.md

@@ -9,19 +9,26 @@ A single-PDF version is also available on arXiv: https://arxiv.org/pdf/2109.0523
 
 # A Short Synopsis
 
-The PBDL book contains a practical and comprehensive introduction of everything related to deep learning in the context of physical simulations. As much as possible, all topics come with hands-on code examples in the form of Jupyter notebooks to quickly get started. Beyond standard supervised learning from data, we’ll look at physical loss constraints, more tightly coupled learning algorithms with differentiable simulations, as well as reinforcement learning and uncertainty modeling. We live in exciting times: these methods have a huge potential to fundamentally change what we can achieve with simulations.
+The PBDL book contains 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. We are living in an era of rapid transformation. These methods have the potential to redefine what’s possible in computational science.
 
 The key aspects that we will address in the following are:
 
-* explain how to use deep learning techniques to solve PDE problems,
+* How to train neural networks to predict the fluid flow around airfoils with diffusion modeling. This gives a probabilistic surrogate model that replaces and outperforms traditional simulators.
-* how to combine them with existing knowledge of physics,
+* How to use model equations as residuals to train networks that represent solutions, and how to improve upon these residual constraints by using differentiable simulations.
-* without discarding our knowledge about numerical methods.
+* How to more tightly interact with a full simulator for inverse problems. E.g., we’ll demonstrate how to circumvent the convergence problems of standard reinforcement learning techniques by leveraging simulators in the training loop.
+* We’ll also discuss the importance of choosing the right network architecture: whether to consider global or local interactions, continuous or discrete representations, and structured versus unstructured graph meshes.
 
 The focus of this book lies on:
 
-* Field-based simulations (not much on Lagrangian methods)
+* how to use deep learning techniques to solve PDE problems,
-* Combinations with deep learning (plenty of other interesting ML techniques exist, but won't be discussed here)
+* how to combine them with existing knowledge of physics,
-* Experiments as are left as an outlook (such as replacing synthetic data with real-world observations)
+* without discarding numerical methods.
+
+At the same time, it’s worth noting what we won’t be covering:
+
+* There’s no in-depth introduction to deep learning and numerical simulations,
+* and the aim is neither a broad survey of research articles in this area.
+
 
 The name of this book, _Physics-based Deep Learning_, denotes combinations of physical modeling and numerical simulations with methods based on artificial neural networks. The general direction of Physics-Based Deep Learning represents a very active, quickly growing and exciting field of research.
 
@@ -29,11 +36,11 @@ The aim is to build on all the powerful numerical techniques that we have at our
 
 The resulting methods have a huge potential to improve what can be done with numerical methods: in scenarios where a solver targets cases from a certain well-defined problem domain repeatedly, it can for instance make a lot of sense to once invest significant resources to train a neural network that supports the repeated solves. Based on the domain-specific specialization of this network, such a hybrid could vastly outperform traditional, generic solvers.
 
+![Divider](resources/divider-gen2.jpg)
 
 # What's new?
 
-* For readers familiar with v0.1 of this text, the [extended section on differentiable physics training](http://physicsbaseddeeplearning.org/diffphys-examples.html)  and the
+What’s new in v0.3? This latest edition takes things even further with a major new chapter on generative modeling, covering cutting-edge techniques like denoising, flow-matching, autoregressive learning, physics-integrated constraints, and diffusion-based graph networks. We’ve also introduced a dedicated section on neural architectures specifically designed for physics simulations. All code examples have been updated to leverage the latest frameworks.
-brand new chapter on [improved learning methods for physics problems](http://physicsbaseddeeplearning.org/diffphys-examples.html) are highly recommended starting points.
 
 
 # Teasers
@@ -50,3 +57,6 @@ https://colab.research.google.com/github/tum-pbs/pbdl-book/blob/main/bayesian-co
 And a notebook to compare proximal policy-based reinforcement learning with physics-based learning for controlling PDEs (spoiler: the physics-aware version does better in the end). Give it a try:
 https://colab.research.google.com/github/tum-pbs/pbdl-book/blob/main/reinflearn-code.ipynb
 
+
+![Divider](resources/divider-gen4.jpg)
+

references.bib

@@ -113,7 +113,7 @@
 
 @inproceedings{franz2023nglobt,
 	  title={Learning to Estimate Single-View Volumetric Flow Motions without 3D Supervision},
-	  author={Erik Franz, Barbara Solenthaler, and Nils Thuerey},
+	  author={Erik Franz and Barbara Solenthaler and Nils Thuerey},
 	  booktitle={ICLR},
 	  year={2023},
 	  url={https://github.com/tum-pbs/Neural-Global-Transport},