Dev Update (#582)

* Fix adaptive refinement (#571)


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Co-authored-by: Dario Coscia <93731561+dario-coscia@users.noreply.github.com>

* Remove collector

* Fixes

* Fixes

* rm unnecessary comment

* fix advection (#581)

* Fix tutorial .html link (#580)

* fix problem data collection for v0.1 (#584)

* Message Passing Module (#516)

* add deep tensor network block

* add interaction network block

* add radial field network block

* add schnet block

* add equivariant network block

* fix + tests + doc files

* fix egnn + equivariance/invariance tests

Co-authored-by: Dario Coscia <dariocos99@gmail.com>

---------

Co-authored-by: giovanni <giovanni.canali98@yahoo.it>
Co-authored-by: AleDinve <giuseppealessio.d@student.unisi.it>

* add type checker (#527)

---------

Co-authored-by: Filippo Olivo <filippo@filippoolivo.com>
Co-authored-by: Giovanni Canali <115086358+GiovanniCanali@users.noreply.github.com>
Co-authored-by: giovanni <giovanni.canali98@yahoo.it>
Co-authored-by: AleDinve <giuseppealessio.d@student.unisi.it>

tutorials/README.md

@@ -9,39 +9,39 @@ The table below provides an overview of each tutorial. All tutorials are also av
 
 | Description   | Tutorial |
 |---------------|-----------|
-Introductory Tutorial: A Beginner’s Guide to PINA|[[.ipynb](tutorial17/tutorial.ipynb),[.py](tutorial17/tutorial.py),[.html](http://mathlab.github.io/PINA/_rst/tutorials/tutorial17/tutorial.html)]|
-How to build a `Problem` in PINA|[[.ipynb](tutorial16/tutorial.ipynb),[.py](tutorial16/tutorial.py),[.html](http://mathlab.github.io/PINA/_rst/tutorials/tutorial16/tutorial.html)]|
-Introduction to Solver classes|[[.ipynb](tutorial18/tutorial.ipynb),[.py](tutorial18/tutorial.py),[.html](http://mathlab.github.io/PINA/_rst/tutorials/tutorial18/tutorial.html)]|
-Introduction to `Trainer` class|[[.ipynb](tutorial11/tutorial.ipynb),[.py](tutorial11/tutorial.py),[.html](http://mathlab.github.io/PINA/_rst/tutorials/tutorial11/tutorial.html)]|
-Data structure for SciML: `Tensor`, `LabelTensor`, `Data` and `Graph` |[[.ipynb](tutorial19/tutorial.ipynb),[.py](tutorial19/tutorial.py),[.html](http://mathlab.github.io/PINA/_rst/tutorials/tutorial19/tutorial.html)]|
-Building geometries with `DomainInterface` class|[[.ipynb](tutorial6/tutorial.ipynb),[.py](tutorial6/tutorial.py),[.html](http://mathlab.github.io/PINA/_rst/tutorials/tutorial6/tutorial.html)]|
-Introduction to PINA `Equation` class|[[.ipynb](tutorial12/tutorial.ipynb),[.py](tutorial12/tutorial.py),[.html](http://mathlab.github.io/PINA/_rst/tutorials/tutorial12/tutorial.html)]|
+Introductory Tutorial: A Beginner’s Guide to PINA|[[.ipynb](tutorial17/tutorial.ipynb),[.py](tutorial17/tutorial.py),[.html](http://mathlab.github.io/PINA/tutorial17/tutorial.html)]|
+How to build a `Problem` in PINA|[[.ipynb](tutorial16/tutorial.ipynb),[.py](tutorial16/tutorial.py),[.html](http://mathlab.github.io/PINA/tutorial16/tutorial.html)]|
+Introduction to Solver classes|[[.ipynb](tutorial18/tutorial.ipynb),[.py](tutorial18/tutorial.py),[.html](http://mathlab.github.io/PINA/tutorial18/tutorial.html)]|
+Introduction to `Trainer` class|[[.ipynb](tutorial11/tutorial.ipynb),[.py](tutorial11/tutorial.py),[.html](http://mathlab.github.io/PINA/tutorial11/tutorial.html)]|
+Data structure for SciML: `Tensor`, `LabelTensor`, `Data` and `Graph` |[[.ipynb](tutorial19/tutorial.ipynb),[.py](tutorial19/tutorial.py),[.html](http://mathlab.github.io/PINA/tutorial19/tutorial.html)]|
+Building geometries with `DomainInterface` class|[[.ipynb](tutorial6/tutorial.ipynb),[.py](tutorial6/tutorial.py),[.html](http://mathlab.github.io/PINA/tutorial6/tutorial.html)]|
+Introduction to PINA `Equation` class|[[.ipynb](tutorial12/tutorial.ipynb),[.py](tutorial12/tutorial.py),[.html](http://mathlab.github.io/PINA/tutorial12/tutorial.html)]|
 
 
 ## Physics Informed Neural Networks
 | Description   | Tutorial  |
 |---------------|-----------|
-Introductory Tutorial:  Physics Informed Neural Networks with PINA |[[.ipynb](tutorial1/tutorial.ipynb),[.py](tutorial1/tutorial.py),[.html](http://mathlab.github.io/PINA/_rst/tutorials/tutorial1/tutorial.html)]|
-Enhancing PINNs with Extra Features to solve the Poisson Problem |[[.ipynb](tutorial2/tutorial.ipynb),[.py](tutorial2/tutorial.py),[.html](http://mathlab.github.io/PINA/_rst/tutorials/tutorial2/tutorial.html)]|
-Applying Hard Constraints in PINNs to solve the Wave Problem |[[.ipynb](tutorial3/tutorial.ipynb),[.py](tutorial3/tutorial.py),[.html](http://mathlab.github.io/PINA/_rst/tutorials/tutorial3/tutorial.html)]|
-Applying Periodic Boundary Conditions in PINNs to solve the Helmotz Problem |[[.ipynb](tutorial9/tutorial.ipynb),[.py](tutorial9/tutorial.py),[.html](http://mathlab.github.io/PINA/_rst/tutorials/tutorial9/tutorial.html)]|
-Inverse Problem Solving with Physics-Informed Neural Network |[[.ipynb](tutorial7/tutorial.ipynb),[.py](tutorial7/tutorial.py),[.html](http://mathlab.github.io/PINA/_rst/tutorials/tutorial7/tutorial.html)]|
-Learning Multiscale PDEs Using Fourier Feature Networks|[[.ipynb](tutorial13/tutorial.ipynb),[.py](tutorial13/tutorial.py),[.html](http://mathlab.github.io/PINA/_rst/tutorials/tutorial13/tutorial.html)]|
-Learning Bifurcating PDE Solutions with Physics-Informed Deep Ensembles|[[.ipynb](tutorial14/tutorial.ipynb),[.py](tutorial14/tutorial.py),[.html](http://mathlab.github.io/PINA/_rst/tutorials/tutorial14/tutorial.html)]|
+Introductory Tutorial:  Physics Informed Neural Networks with PINA |[[.ipynb](tutorial1/tutorial.ipynb),[.py](tutorial1/tutorial.py),[.html](http://mathlab.github.io/PINA/tutorial1/tutorial.html)]|
+Enhancing PINNs with Extra Features to solve the Poisson Problem |[[.ipynb](tutorial2/tutorial.ipynb),[.py](tutorial2/tutorial.py),[.html](http://mathlab.github.io/PINA/tutorial2/tutorial.html)]|
+Applying Hard Constraints in PINNs to solve the Wave Problem |[[.ipynb](tutorial3/tutorial.ipynb),[.py](tutorial3/tutorial.py),[.html](http://mathlab.github.io/PINA/tutorial3/tutorial.html)]|
+Applying Periodic Boundary Conditions in PINNs to solve the Helmholtz Problem |[[.ipynb](tutorial9/tutorial.ipynb),[.py](tutorial9/tutorial.py),[.html](http://mathlab.github.io/PINA/tutorial9/tutorial.html)]|
+Inverse Problem Solving with Physics-Informed Neural Network |[[.ipynb](tutorial7/tutorial.ipynb),[.py](tutorial7/tutorial.py),[.html](http://mathlab.github.io/PINA/tutorial7/tutorial.html)]|
+Learning Multiscale PDEs Using Fourier Feature Networks|[[.ipynb](tutorial13/tutorial.ipynb),[.py](tutorial13/tutorial.py),[.html](http://mathlab.github.io/PINA/tutorial13/tutorial.html)]|
+Learning Bifurcating PDE Solutions with Physics-Informed Deep Ensembles|[[.ipynb](tutorial14/tutorial.ipynb),[.py](tutorial14/tutorial.py),[.html](http://mathlab.github.io/PINA/tutorial14/tutorial.html)]|
 
 
 ## Neural Operator Learning
 | Description   | Tutorial  |
 |---------------|-----------|
-Introductory Tutorial: Neural Operator Learning with PINA |[[.ipynb](tutorial21/tutorial.ipynb),[.py](tutorial21/tutorial.py),[.html](http://mathlab.github.io/PINA/_rst/tutorials/tutorial21/tutorial.html)]|
-Modeling 2D Darcy Flow with the Fourier Neural Operator |[[.ipynb](tutorial5/tutorial.ipynb),[.py](tutorial5/tutorial.py),[.html](http://mathlab.github.io/PINA/_rst/tutorials/tutorial5/tutorial.html)]|
-Solving the Kuramoto–Sivashinsky Equation with Averaging Neural Operator |[[.ipynb](tutorial10/tutorial.ipynb),[.py](tutorial10/tutorial.py),[.html](http://mathlab.github.io/PINA/_rst/tutorials/tutorial10/tutorial.html)]|
+Introductory Tutorial: Neural Operator Learning with PINA |[[.ipynb](tutorial21/tutorial.ipynb),[.py](tutorial21/tutorial.py),[.html](http://mathlab.github.io/PINA/tutorial21/tutorial.html)]|
+Modeling 2D Darcy Flow with the Fourier Neural Operator |[[.ipynb](tutorial5/tutorial.ipynb),[.py](tutorial5/tutorial.py),[.html](http://mathlab.github.io/PINA/tutorial5/tutorial.html)]|
+Solving the Kuramoto–Sivashinsky Equation with Averaging Neural Operator |[[.ipynb](tutorial10/tutorial.ipynb),[.py](tutorial10/tutorial.py),[.html](http://mathlab.github.io/PINA/tutorial10/tutorial.html)]|
 
 ## Supervised Learning
 | Description   | Tutorial  |
 |---------------|-----------|
-Introductory Tutorial: Supervised Learning with PINA |[[.ipynb](tutorial20/tutorial.ipynb),[.py](tutorial20/tutorial.py),[.html](http://mathlab.github.io/PINA/_rst/tutorials/tutorial20/tutorial.html)]|
-Chemical Properties Prediction with Graph Neural Networks |[[.ipynb](tutorial15/tutorial.ipynb),[.py](tutorial15/tutorial.py),[.html](http://mathlab.github.io/PINA/_rst/tutorials/tutorial15/tutorial.html)]|
-Unstructured Convolutional Autoencoders with Continuous Convolution |[[.ipynb](tutorial4/tutorial.ipynb),[.py](tutorial4/tutorial.py),[.html](http://mathlab.github.io/PINA/_rst/tutorials/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/_rst/tutorials/tutorial8/tutorial.html)]|
+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)]|
+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)]|