Colab tutorials (#367)

* Colab Button & Run added

tutorials/tutorial10/tutorial.ipynb

@@ -6,6 +6,8 @@
    "source": [
     "# Tutorial: Averaging Neural Operator for solving Kuramoto Sivashinsky equation\n",
     "\n",
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial10/tutorial.ipynb)\n",
+    "\n",
     "In this tutorial we will build a Neural Operator using the\n",
     "`AveragingNeuralOperator` model and the `SupervisedSolver`. At the end of the\n",
     "tutorial you will be able to train a Neural Operator for learning\n",
@@ -21,6 +23,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "## routine needed to run the notebook on Google Colab\n",
+    "try:\n",
+    "  import google.colab\n",
+    "  IN_COLAB = True\n",
+    "except:\n",
+    "  IN_COLAB = False\n",
+    "if IN_COLAB:\n",
+    "  !pip install \"pina-mathlab\"\n",
+    "\n",
     "import torch\n",
     "import matplotlib.pyplot as plt\n",
     "from scipy import io\n",

tutorials/tutorial10/tutorial.py

@@ -3,6 +3,8 @@
 
 # # Tutorial: Averaging Neural Operator for solving Kuramoto Sivashinsky equation
 # 
+# [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](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 learning
@@ -15,6 +17,15 @@
 # In[1]:
 
 
+## routine needed to run the notebook on Google Colab
+try:
+  import google.colab
+  IN_COLAB = True
+except:
+  IN_COLAB = False
+if IN_COLAB:
+  get_ipython().system('pip install "pina-mathlab"')
+
 import torch
 import matplotlib.pyplot as plt
 from scipy import io
@@ -247,6 +258,6 @@ with torch.no_grad():
 # 
 # 1. Train the network for longer or with different layer sizes and assert the finaly accuracy
 # 
-# 2. We left a more challenging dataset [Data_KS2.mat](tutorial10/dat/Data_KS2.mat) where $A_k \in [-0.5, 0.5]$, $\ell_k \in [1, 2, 3]$, $\phi_k \in [0, 2\pi]$ for loger training
+# 2. We left a more challenging dataset [Data_KS2.mat](dat/Data_KS2.mat) where $A_k \in [-0.5, 0.5]$, $\ell_k \in [1, 2, 3]$, $\phi_k \in [0, 2\pi]$ for loger training
 # 
 # 3. Compare the performance between the different neural operators (you can even try to implement your favourite one!)