From 6792d75b13d00106ee140e049b41d360cacf8d29 Mon Sep 17 00:00:00 2001 From: NT Date: Mon, 25 Apr 2022 13:30:10 +0200 Subject: [PATCH] updated readme for v0.2 --- README.md | 13 +++++++++++-- 1 file changed, 11 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index cf8788c..b0c4647 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,4 @@ -# Welcome to the Physics-based Deep Learning book (PBDL) +# Welcome to the Physics-based Deep Learning book (PBDL) v0.2 This is the source code repository for the Jupyter book "Physics-based Deep Learning". You can find the full, readable version online at: [https://physicsbaseddeeplearning.org/](https://physicsbaseddeeplearning.org/) @@ -21,7 +21,7 @@ The focus of this book lies on: * Field-based simulations (not much on Lagrangian methods) * Combinations with deep learning (plenty of other interesting ML techniques exist, but won't be discussed here) -* Experiments as are left as an outlook (i.e., replacing synthetic data with real-world observations) +* Experiments as are left as an outlook (such as replacing synthetic data with real-world observations) 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 +29,20 @@ 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. + +# What's new? + +* For readers familiar with v0.1 of this text, the brand new chapter on improved learning methods for physics problems is highly recommended: starting with https://www.physicsbaseddeeplearning.org/physgrad.html + + # Teasers To mention a few highlights: the book contains a notebook to train hybrid fluid flow (Navier-Stokes) solvers via differentiable physics to reduce numerical errors. Try it out: https://colab.research.google.com/github/tum-pbs/pbdl-book/blob/main/diffphys-code-sol.ipynb +In v0.2 there's new notebook for an improved learning scheme which jointly computes update directions for neural networks and physics (via half-inverse gradients): +https://colab.research.google.com/github/tum-pbs/pbdl-book/blob/main/physgrad-hig-code.ipynb + It also has example code to train a Bayesian Neural Network for RANS flow predictions around airfoils that yield uncertainty estimates. You can run the code right away here: https://colab.research.google.com/github/tum-pbs/pbdl-book/blob/main/bayesian-code.ipynb