tut22 (#637)

Co-authored-by: Federico Pichi <fpichi@sissa.it>

docs/source/_tutorial.rst

@@ -39,5 +39,6 @@ Supervised Learning
 
 - `Introductory Tutorial: Supervised Learning with PINA  <tutorial20/tutorial.html>`_
 - `Chemical Properties Prediction with Graph Neural Networks <tutorial15/tutorial.html>`_
+- `Reduced Order Model with Graph Neural Networks for Unstructured Domains <tutorial22/tutorial.html>`_
 - `Unstructured Convolutional Autoencoders with Continuous Convolution <tutorial4/tutorial.html>`_
 - `Reduced Order Modeling with POD-RBF and POD-NN Approaches for Fluid Dynamics <tutorial8/tutorial.html>`_

tutorials/README.md

@@ -42,6 +42,7 @@ Solving the Kuramoto–Sivashinsky Equation with Averaging Neural Operator |[[.i
 |---------------|-----------|
 Introductory Tutorial: Supervised Learning with PINA |[[.ipynb](tutorial20/tutorial.ipynb),[.py](tutorial20/tutorial.py),[.html](http://mathlab.github.io/PINA/tutorial20/tutorial.html)]|
 Chemical Properties Prediction with Graph Neural Networks |[[.ipynb](tutorial15/tutorial.ipynb),[.py](tutorial15/tutorial.py),[.html](http://mathlab.github.io/PINA/tutorial15/tutorial.html)]|
+Reduced Order Model with Graph Neural Networks for Unstructured Domains| [[.ipynb](tutorial22/tutorial.ipynb),[.py](tutorial22/tutorial.py),[.html](http://mathlab.github.io/PINA/tutorial22/tutorial.html)]|
 Unstructured Convolutional Autoencoders with Continuous Convolution |[[.ipynb](tutorial4/tutorial.ipynb),[.py](tutorial4/tutorial.py),[.html](http://mathlab.github.io/PINA/tutorial4/tutorial.html)]|
 Reduced Order Modeling with POD-RBF and POD-NN Approaches for Fluid Dynamics| [[.ipynb](tutorial8/tutorial.ipynb),[.py](tutorial8/tutorial.py),[.html](http://mathlab.github.io/PINA/tutorial8/tutorial.html)]|