diff --git a/PINN.yml b/PINN.yml
new file mode 100644
index 0000000..e7b3d46
--- /dev/null
+++ b/PINN.yml
@@ -0,0 +1,15 @@
+name: CNN
+channels:
+  - conda-forge
+  - defaults
+dependencies:
+  - python
+  - keras
+  - tensorflow=2.3
+  - pydot
+  - graphviz
+  - ipdb
+  - matplotlib=3.0
+  - basemap-data-hires
+  - geopy
+  - notebook
diff --git a/PINNs b/PINNs
new file mode 160000
index 0000000..0542794
--- /dev/null
+++ b/PINNs
@@ -0,0 +1 @@
+Subproject commit 0542794b0a91b9e8764a38f5fc9cd9647a3929ba
diff --git a/Physics_Informed_NNs.ipynb b/Physics_Informed_NNs.ipynb
index 1d714f5..df2438b 100644
--- a/Physics_Informed_NNs.ipynb
+++ b/Physics_Informed_NNs.ipynb
@@ -16,14 +16,56 @@
    "id": "1cf2aeb8",
    "metadata": {},
    "source": [
-    "# Overview"
+    "# Overview\n",
+    "\n",
+    "This notebook is based on two papers: *[Physics-Informed Neural Networks:  A Deep LearningFramework for Solving Forward and Inverse ProblemsInvolving Nonlinear Partial Differential Equations](https://www.sciencedirect.com/science/article/pii/S0021999118307125)* and *[Hidden Physics Models:  Machine Learning of NonlinearPartial Differential Equations](https://www.sciencedirect.com/science/article/pii/S0021999117309014)* with the help of  Fergus Shone and Michael Macraild."
    ]
   },
   {
    "cell_type": "markdown",
    "id": "10361a00",
    "metadata": {},
-   "source": []
+   "source": [
+    "<div style=\"background-color: #ccffcc; padding: 10px;\">\n",
+    "\n",
+    "<h1>Physics Informed Neural Networks</h1>\n",
+    "\n",
+    "\n",
+    " \n",
+    "## The very basics\n",
+    "\n",
+    "If you know nothing about neural networks there is a [toy neural network python code example](https://github.com/cemac/LIFD_ENV_ML_NOTEBOOKS/tree/main/ToyNeuralNetwork) included in the [LIFD ENV ML Notebooks Repository]( https://github.com/cemac/LIFD_ENV_ML_NOTEBOOKS). Creating a 2 layer neural network to illustrate the fundamentals of how Neural Networks work and the equivlent code using the python machine learning library [tensorflow](https://keras.io/). \n",
+    "\n",
+    "    \n",
+    "## Recommended reading \n",
+    "    \n",
+    "The in-depth theory behind neural networks will not be covered here as this tutorial is focusing on application of machine learning methods. If you wish to learn more here are some great starting points.     \n",
+    "\n",
+    "</div>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0c2d1f44",
+   "metadata": {},
+   "source": [
+    "<hr>\n",
+    "\n",
+    "\n",
+    "<div style=\"background-color: #e6ccff; padding: 10px;\">\n",
+    "    \n",
+    "<h1> Machine Learning Theory </h1>\n",
+    "<a href=\"\">\n",
+    "<img src=\"\">\n",
+    "</a>\n",
+    "\n",
+    "    \n",
+    "## Neural Networks\n",
+    "\n",
+    "  \n",
+    "    \n",
+    "</div>    "
+   ]
   },
   {
    "cell_type": "markdown",