Colab tutorials (#367)

* Colab Button & Run added

Author: AleDinve

docs/source/_rst/tutorials/tutorial1/tutorial.rst

@@ -1,6 +1,11 @@
 Tutorial: Physics Informed Neural Networks on PINA
 ==================================================
 
+|Open In Colab|
+
+.. |Open In Colab| image:: https://colab.research.google.com/assets/colab-badge.svg
+   :target: https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial1/tutorial.ipynb
+
 In this tutorial, we will demonstrate a typical use case of **PINA** on
 a toy problem, following the standard API procedure.
 
@@ -74,6 +79,15 @@ What if our equation is also time-dependent? In this case, our ``class``
 will inherit from both ``SpatialProblem`` and ``TimeDependentProblem``:
 
 .. code:: ipython3
+    
+    ## routine needed to run the notebook on Google Colab
+    try:
+      import google.colab
+      IN_COLAB = True
+    except:
+      IN_COLAB = False
+    if IN_COLAB:
+      !pip install "pina-mathlab"
 
     from pina.problem import SpatialProblem, TimeDependentProblem
     from pina.geometry import CartesianDomain

docs/source/_rst/tutorials/tutorial10/tutorial.rst

@@ -1,6 +1,11 @@
 Tutorial: Averaging Neural Operator for solving Kuramoto Sivashinsky equation
 =============================================================================
 
+|Open In Colab|
+
+.. |Open In Colab| image:: https://colab.research.google.com/assets/colab-badge.svg
+   :target: https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial10/tutorial.ipynb
+
 In this tutorial we will build a Neural Operator using the
 ``AveragingNeuralOperator`` model and the ``SupervisedSolver``. At the
 end of the tutorial you will be able to train a Neural Operator for
@@ -11,6 +16,15 @@ operations.
 
 .. code:: ipython3
 
+    ## routine needed to run the notebook on Google Colab
+    try:
+      import google.colab
+      IN_COLAB = True
+    except:
+      IN_COLAB = False
+    if IN_COLAB:
+      !pip install "pina-mathlab"
+    
     import torch
     import matplotlib.pyplot as plt
     from scipy import io

docs/source/_rst/tutorials/tutorial11/tutorial.rst

@@ -1,6 +1,11 @@
 Tutorial: PINA and PyTorch Lightning, training tips and visualizations
 ======================================================================
 
+|Open In Colab|
+
+.. |Open In Colab| image:: https://colab.research.google.com/assets/colab-badge.svg
+   :target: https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial11/tutorial.ipynb
+
 In this tutorial, we will delve deeper into the functionality of the
 ``Trainer`` class, which serves as the cornerstone for training **PINA**
 `Solvers <https://mathlab.github.io/PINA/_rst/_code.html#solvers>`__.
@@ -18,6 +23,15 @@ problem and the ``PINN`` solver.
 
 .. code:: ipython3
 
+    ## routine needed to run the notebook on Google Colab
+    try:
+      import google.colab
+      IN_COLAB = True
+    except:
+      IN_COLAB = False
+    if IN_COLAB:
+      !pip install "pina-mathlab"
+    
     import torch
     
     from pina import Condition, Trainer

docs/source/_rst/tutorials/tutorial12/tutorial.rst

@@ -1,6 +1,11 @@
 Tutorial: The ``Equation`` Class
 ================================
 
+|Open In Colab|
+
+.. |Open In Colab| image:: https://colab.research.google.com/assets/colab-badge.svg
+   :target: https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial12/tutorial.ipynb
+
 In this tutorial, we will show how to use the ``Equation`` Class in
 PINA. Specifically, we will see how use the Class and its inherited
 classes to enforce residuals minimization in PINNs.
@@ -30,6 +35,15 @@ class.
 
 .. code:: ipython3
 
+    ## routine needed to run the notebook on Google Colab
+    try:
+      import google.colab
+      IN_COLAB = True
+    except:
+      IN_COLAB = False
+    if IN_COLAB:
+      !pip install "pina-mathlab"
+    
     #useful imports
     from pina.problem import SpatialProblem, TimeDependentProblem
     from pina.equation import Equation, FixedValue, FixedGradient, FixedFlux

docs/source/_rst/tutorials/tutorial13/tutorial.rst

@@ -1,6 +1,11 @@
 Tutorial: Multiscale PDE learning with Fourier Feature Network
 ==============================================================
 
+|Open In Colab|
+
+.. |Open In Colab| image:: https://colab.research.google.com/assets/colab-badge.svg
+   :target: https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial13/tutorial.ipynb
+
 This tutorial presents how to solve with Physics-Informed Neural
 Networks (PINNs) a PDE characterized by multiscale behaviour, as
 presented in `On the eigenvector bias of Fourier feature networks: From
@@ -11,6 +16,15 @@ First of all, some useful imports.
 
 .. code:: ipython3
 
+    ## routine needed to run the notebook on Google Colab
+    try:
+      import google.colab
+      IN_COLAB = True
+    except:
+      IN_COLAB = False
+    if IN_COLAB:
+      !pip install "pina-mathlab"
+    
     import torch
     
     from pina import Condition, Plotter, Trainer, Plotter

docs/source/_rst/tutorials/tutorial2/tutorial.rst

@@ -1,6 +1,12 @@
 Tutorial: Two dimensional Poisson problem using Extra Features Learning
 =======================================================================
 
+|Open In Colab|
+
+.. |Open In Colab| image:: https://colab.research.google.com/assets/colab-badge.svg
+   :target: https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial2/tutorial.ipynb
+
+
 This tutorial presents how to solve with Physics-Informed Neural
 Networks (PINNs) a 2D Poisson problem with Dirichlet boundary
 conditions. We will train with standard PINN’s training, and with
@@ -13,6 +19,15 @@ First of all, some useful imports.
 
 .. code:: ipython3
 
+    ## routine needed to run the notebook on Google Colab
+    try:
+      import google.colab
+      IN_COLAB = True
+    except:
+      IN_COLAB = False
+    if IN_COLAB:
+      !pip install "pina-mathlab"
+
     import torch
     from torch.nn import Softplus
     

docs/source/_rst/tutorials/tutorial3/tutorial.rst

@@ -1,6 +1,12 @@
 Tutorial: Two dimensional Wave problem with hard constraint
 ===========================================================
 
+|Open In Colab|
+
+.. |Open In Colab| image:: https://colab.research.google.com/assets/colab-badge.svg
+   :target: https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial3/tutorial.ipynb
+
+
 In this tutorial we present how to solve the wave equation using hard
 constraint PINNs. For doing so we will build a costum ``torch`` model
 and pass it to the ``PINN`` solver.
@@ -9,6 +15,15 @@ First of all, some useful imports.
 
 .. code:: ipython3
 
+    ## routine needed to run the notebook on Google Colab
+    try:
+      import google.colab
+      IN_COLAB = True
+    except:
+      IN_COLAB = False
+    if IN_COLAB:
+      !pip install "pina-mathlab"
+
     import torch
     
     from pina.problem import SpatialProblem, TimeDependentProblem

docs/source/_rst/tutorials/tutorial4/tutorial.rst

@@ -1,6 +1,11 @@
 Tutorial: Unstructured convolutional autoencoder via continuous convolution
 ===========================================================================
 
+|Open In Colab|
+
+.. |Open In Colab| image:: https://colab.research.google.com/assets/colab-badge.svg
+   :target: https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial4/tutorial.ipynb
+
 In this tutorial, we will show how to use the Continuous Convolutional
 Filter, and how to build common Deep Learning architectures with it. The
 implementation of the filter follows the original work `A Continuous
@@ -11,6 +16,15 @@ First of all we import the modules needed for the tutorial:
 
 .. code:: ipython3
 
+    ## routine needed to run the notebook on Google Colab
+    try:
+      import google.colab
+      IN_COLAB = True
+    except:
+      IN_COLAB = False
+    if IN_COLAB:
+      !pip install "pina-mathlab"
+    
     import torch 
     import matplotlib.pyplot as plt 
     from pina.problem import AbstractProblem

docs/source/_rst/tutorials/tutorial5/tutorial.rst

@@ -1,6 +1,11 @@
 Tutorial: Two dimensional Darcy flow using the Fourier Neural Operator
 ======================================================================
 
+|Open In Colab|
+
+.. |Open In Colab| image:: https://colab.research.google.com/assets/colab-badge.svg
+   :target: https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial5/tutorial.ipynb
+
 In this tutorial we are going to solve the Darcy flow problem in two
 dimensions, presented in `Fourier Neural Operator for Parametric Partial
 Differential Equation <https://openreview.net/pdf?id=c8P9NQVtmnO>`__.
@@ -9,6 +14,16 @@ First of all we import the modules needed for the tutorial. Importing
 
 .. code:: ipython3
 
+    ## routine needed to run the notebook on Google Colab
+    try:
+      import google.colab
+      IN_COLAB = True
+    except:
+      IN_COLAB = False
+    if IN_COLAB:
+      !pip install "pina-mathlab"
+      !pip install scipy
+      
     # !pip install scipy  # install scipy
     from scipy import io
     import torch

docs/source/_rst/tutorials/tutorial6/tutorial.rst

@@ -1,6 +1,11 @@
 Tutorial: Building custom geometries with PINA ``Location`` class
 =================================================================
 
+|Open In Colab|
+
+.. |Open In Colab| image:: https://colab.research.google.com/assets/colab-badge.svg
+   :target: https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial6/tutorial.ipynb
+
 In this tutorial we will show how to use geometries in PINA.
 Specifically, the tutorial will include how to create geometries and how
 to visualize them. The topics covered are:
@@ -13,6 +18,15 @@ We import the relevant modules first.
 
 .. code:: ipython3
 
+    ## routine needed to run the notebook on Google Colab
+    try:
+      import google.colab
+      IN_COLAB = True
+    except:
+      IN_COLAB = False
+    if IN_COLAB:
+      !pip install "pina-mathlab"
+
     import matplotlib.pyplot as plt
     from pina.geometry import EllipsoidDomain, Difference, CartesianDomain, Union, SimplexDomain
     from pina.label_tensor import LabelTensor