[doc] Update publications & documentation links in README.md

README.md

@@ -50,9 +50,10 @@ See the [detailed installation instructions](https://tum-pbs.github.io/PhiFlow/I
 
 To get started, check out our YouTube tutorial series and the following Jupyter notebooks:
 
-* [<img src="https://www.tensorflow.org/images/colab_logo_32px.png" height=16> Fluids](https://colab.research.google.com/github/tum-pbs/PhiFlow/blob/develop/docs/Fluids_Tutorial.ipynb): Introduction to core classes and fluid-related functions.
-* [<img src="https://www.tensorflow.org/images/colab_logo_32px.png" height=16> Solar System](https://colab.research.google.com/github/tum-pbs/PhiFlow/blob/develop/docs/Planets_Tutorial.ipynb): Visualize a many-body system with Newtonian gravity.
-* [<img src="https://www.tensorflow.org/images/colab_logo_32px.png" height=16> Learning to Throw](https://colab.research.google.com/github/tum-pbs/PhiFlow/blob/develop/docs/Learn_to_Throw_Tutorial.ipynb): Train a neural network to hit a target, comparing supervised and differentiable physics losses.
+* [<img src="https://www.tensorflow.org/images/colab_logo_32px.png" height=16>](https://colab.research.google.com/github/tum-pbs/PhiFlow/blob/develop/docs/Math_Introduction.ipynb) [Tensors](https://tum-pbs.github.io/PhiFlow/Math_Introduction.html): Introduction to tensors.
+* [<img src="https://www.tensorflow.org/images/colab_logo_32px.png" height=16>](https://colab.research.google.com/github/tum-pbs/PhiFlow/blob/develop/docs/Fluids_Tutorial.ipynb) [Fluids](https://tum-pbs.github.io/PhiFlow/Fluids_Tutorial.html): Introduction to core classes and fluid-related functions.
+* [<img src="https://www.tensorflow.org/images/colab_logo_32px.png" height=16>](https://colab.research.google.com/github/tum-pbs/PhiFlow/blob/develop/docs/Planets_Tutorial.ipynb) [Solar System](https://tum-pbs.github.io/PhiFlow/Planets_Tutorial.html): Visualize a many-body system with Newtonian gravity.
+* [<img src="https://www.tensorflow.org/images/colab_logo_32px.png" height=16>](https://colab.research.google.com/github/tum-pbs/PhiFlow/blob/develop/docs/Learn_to_Throw_Tutorial.ipynb) [Learn to Throw](https://tum-pbs.github.io/PhiFlow/Learn_to_Throw_Tutorial.html): Train a neural network to hit a target, comparing supervised and differentiable physics losses.
 
 If you like to work with an IDE, like PyCharm or VS Code, the following demos will also be helpful:
 
@@ -61,13 +62,20 @@ If you like to work with an IDE, like PyCharm or VS Code, the following demos wi
 
 ## Publications
 
-We have recently submitted a whitepaper.
+We will upload a whitepaper, soon.
 In the meantime, please cite the ICLR 2020 paper.
 
 * [Learning to Control PDEs with Differentiable Physics](https://ge.in.tum.de/publications/2020-iclr-holl/), *Philipp Holl, Vladlen Koltun, Nils Thuerey*, ICLR 2020.
 * [Solver-in-the-Loop: Learning from Differentiable Physics to Interact with Iterative PDE-Solvers](https://arxiv.org/abs/2007.00016), *Kiwon Um, Raymond Fei, Philipp Holl, Robert Brand, Nils Thuerey*, NeurIPS 2020.
 * [Φ<sub>Flow</sub>: A Differentiable PDE Solving Framework for Deep Learning via Physical Simulations](https://montrealrobotics.ca/diffcvgp/), *Nils Thuerey, Kiwon Um, Philipp Holl*, DiffCVGP workshop at NeurIPS 2020.
+* [Physics-based Deep Learning](https://physicsbaseddeeplearning.org/intro.html) (book), *Nils Thuerey, Philipp Holl, Maximilian Mueller, Patrick Schnell, Felix Trost, Kiwon Um*, DiffCVGP workshop at NeurIPS 2020.
 * [Half-Inverse Gradients for Physical Deep Learning](https://arxiv.org/abs/2203.10131), *Patrick Schnell, Philipp Holl, Nils Thuerey*, ICLR 2022.
+* [Scale-invariant Learning by Physics Inversion](https://arxiv.org/abs/2109.15048), *Philipp Holl, Vladlen Koltun, Nils Thuerey*, NeurIPS 2022.
+
+Φ<sub>Flow</sub> has been used in the following data sets:
+
+* [PDEBench](https://github.com/pdebench/PDEBench)
+* [PDEarena](https://microsoft.github.io/pdearena/)
 
 ## Version History