Merge pull request #9 from aswinkumar1999/main

Updated to Latest Modulus Documention links

workspace/python/Start_Here.ipynb

@@ -125,7 +125,7 @@
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workspace/python/jupyter_notebook/FourCastNet/Forecasting-weather-FourCastNet.ipynb

@@ -457,7 +457,7 @@
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    "pygments_lexer": "ipython3",
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workspace/python/jupyter_notebook/Operators/Darcy_Flow_using_Adaptive_Fourier_Neural_Operators.ipynb

@@ -422,7 +422,7 @@
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workspace/python/jupyter_notebook/Operators/Darcy_Flow_using_Fourier_Neural_Operators.ipynb

@@ -496,7 +496,7 @@
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    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
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workspace/python/jupyter_notebook/diffusion_1d/Diffusion_Problem_Notebook.ipynb

@@ -560,7 +560,7 @@
    "source": [
     "### Step 5: Hydra configuration\n",
     "\n",
-    "More information on the available configurations can be found in [Modulus Configuration](https://docs.nvidia.com/deeplearning/modulus/text/features/configuration.html)."
+    "More information on the available configurations can be found in [Modulus Configuration](https://docs.nvidia.com/deeplearning/modulus/modulus-sym/user_guide/features/configuration.html)."
    ]
   },
   {
@@ -952,7 +952,7 @@
    "source": [
     "### Step 5: Hydra configuration - Parameterized\n",
     "\n",
-    "More information on the available configurations can be found in [Modulus Configuration](https://docs.nvidia.com/deeplearning/modulus/text/features/configuration.html)."
+    "More information on the available configurations can be found in [Modulus Configuration](https://docs.nvidia.com/deeplearning/modulus/modulus-sym/user_guide/features/configuration.html)."
    ]
   },
   {
@@ -1036,7 +1036,7 @@
    "source": [
     "### Visualizing the solution - Parameterized\n",
     "\n",
-    "The <code>.npz</code> arrays can be plotted similarly to the previous section to visualize the simulation output. You can see that we get the same answer as the analytical solution. You can try to run the problem in <code>eval</code> mode by changing the validation data and see how it performs for the other <code>D1</code> values as well. To run the model in evaluation mode (i.e. without training), you just need to <a href=\"https://docs.nvidia.com/deeplearning/modulus/text/features/configuration.html#run-modes\" rel=\"nofollow\">modify the config file</a>.\n",
+    "The <code>.npz</code> arrays can be plotted similarly to the previous section to visualize the simulation output. You can see that we get the same answer as the analytical solution. You can try to run the problem in <code>eval</code> mode by changing the validation data and see how it performs for the other <code>D1</code> values as well. To run the model in evaluation mode (i.e. without training), you just need to <a href=\"https://docs.nvidia.com/deeplearning/modulus/modulus-sym/user_guide/features/configuration.html#run-modes\" rel=\"nofollow\">modify the config file</a>.\n",
     "\n",
     "<img src=\"images/image_diffusion_problem_bootcamp_parameterized.png\" alt=\"Drawing\" style=\"width:500px\" />\n"
    ]
@@ -1106,7 +1106,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
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workspace/python/jupyter_notebook/navier_stokes/Weather-forecasting-navier-stokes.ipynb

@@ -419,7 +419,7 @@
    "source": [
     "### Step 5: Hydra configuration\n",
     "\n",
-    "More information on the available configurations can be found in [Modulus Configuration](https://docs.nvidia.com/deeplearning/modulus/text/features/configuration.html)."
+    "More information on the available configurations can be found in [Modulus Configuration](https://docs.nvidia.com/deeplearning/modulus/modulus-sym/user_guide/features/configuration.html)."
    ]
   },
   {
@@ -616,7 +616,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.9"
+   "version": "3.10.12"
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workspace/python/jupyter_notebook/projectile/Getting_Started_Projectile.ipynb

@@ -119,7 +119,7 @@
    "source": [
     "From v22.03 modulus supports APIs for solving data-driven problems where the data is available on grid/mesh as well as the PINN problems where point clouds are used for problem formulation. In this section, since we will be solving the projectile problem using the PINN approach, we will use the functions and classes from the `modulus.solver` module. The `Solver` class trains and evaluates the Modulus' neural network solver. The `Domain` class is used to add constraints, monitors, validators, etc. to the problem.\n",
     "\n",
-    "The `geometry` module contains predefined geometries respectively that one can use to define the problem. We will describe each of them in detail as we move forward in the code. For more detailed information on all the different modules, we recommend you to refer the [Modulus API Documentation](https://docs.nvidia.com/deeplearning/modulus/api_source/api_index.html).\n"
+    "The `geometry` module contains predefined geometries respectively that one can use to define the problem. We will describe each of them in detail as we move forward in the code. For more detailed information on all the different modules, we recommend you to refer the [Modulus API Documentation](https://docs.nvidia.com/deeplearning/modulus/modulus-sym/api/api_index.html)."
    ]
   },
   {
@@ -196,7 +196,7 @@
     "\n",
     "Note that we moved all the terms of the PDE either to LHS or RHS. This way, while using the equations in the constraints, we can assign a custom source function to the <code>’ode_y’</code> or <code>’ode_x’</code> key instead of 0 to add more source terms to our PDE.\n",
     "Great! We just wrote our own PDE in Modulus! Once you have understood the process to code a simple PDE, you can easily extend the procedure for different PDEs. You can also bundle multiple PDEs together in the same file by adding new keys to the equations dictionary. \n",
-    "<em>(More examples of coding your own PDE can be found in the <a href=\"https://docs.nvidia.com/deeplearning/modulus/text/foundational/1d_wave_equation.html\" rel=\"nofollow\">Modulus User Documentation</a>)</em>. \n"
+    "<em>(More examples of coding your own PDE can be found in the <a href=\"https://docs.nvidia.com/deeplearning/modulus/modulus-sym/user_guide/foundational/1d_wave_equation.html\" rel=\"nofollow\">Modulus User Documentation</a>)</em>. \n"
    ]
   },
   {
@@ -384,7 +384,7 @@
    "source": [
     "### Step 5: Using Hydra to configure Modulus\n",
     "\n",
-    "At the heart of using Modulus are Hydra config files. The one for this example can be found in the code shown below. Using hydra allows a highly customizable but user-friendly method for configuring the majority of Modulus’ features. More information can be found in <a href=\"https://docs.nvidia.com/deeplearning/modulus/text/features/configuration.html\" rel=\"nofollow\">Modulus Configuration</a>.\n"
+    "At the heart of using Modulus are Hydra config files. The one for this example can be found in the code shown below. Using hydra allows a highly customizable but user-friendly method for configuring the majority of Modulus’ features. More information can be found in <a href=\"https://docs.nvidia.com/deeplearning/modulus/modulus-sym/user_guide/features/configuration.html\" rel=\"nofollow\">Modulus Configuration</a>.\n"
    ]
   },
   {
@@ -459,7 +459,8 @@
    "source": [
     "Awesome! We have just completed the file set-up for the problem using the Modulus library. We are now ready to solve the PDEs using Neural Networks!\n",
     "\n",
-    "Before we can start training, we can make use of Tensorboard for visualizing the loss values and convergence of several other monitors we just created. This can be done inside the Jupyter framework by selecting the directory in which the checkpoint will be stored by clicking on the small checkbox next to it. The option to launch a Tensorboard then shows up in that directory. \n",
+    "Before we can start training, we can make use of Tensorboard for visualizing the loss values and convergence of several other monitors we just created. This can be done inside the Jupyter framework by selecting the directory in which the checkpoint will be stored by clicking on the small checkbox next to it. The option to launch a Tensorboard then shows up in that directory.\n",
+    "\n",
     "<img src=\"images/tensorboard.png\" alt=\"Drawing\" style=\"width:900px\" />\n",
     "\n",
     "Also, as Modulus uses Hydra for config management, this causes issues when the code is directly executed through the jupyter notebook. Hence, we will save the code in form of a python script and run that script inside the jupyter cell. This example is already saved for you, and the code block below executes that script <a href=\"../../source_code/projectile/projectile.py\" rel=\"nofollow\"><code>projectile.py</code></a>. You are encouraged to open the script and review the code once before executing. Also, feel free to edit the parameters in the <a href=\"../../source_code/projectile/conf/config.yaml\" rel=\"nofollow\"><code>config.yaml</code></a> file of the model and see its effect on the results.\n"
@@ -567,7 +568,7 @@
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    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
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workspace/python/jupyter_notebook/spring_mass/Spring_Mass_Problem_Notebook.ipynb

@@ -307,7 +307,7 @@
    "source": [
     "### Step 5: Hydra configuration\n",
     "\n",
-    "More information on the available configurations can be found in [Modulus Configuration](https://docs.nvidia.com/deeplearning/modulus/text/features/configuration.html)."
+    "More information on the available configurations can be found in [Modulus Configuration](https://docs.nvidia.com/deeplearning/modulus/modulus-sym/user_guide/features/configuration.html)."
    ]
   },
   {
@@ -658,7 +658,7 @@
    "source": [
     "### Step 5: Hydra configuration\n",
     "\n",
-    "More information on the available configurations can be found in [Modulus Configuration](https://docs.nvidia.com/deeplearning/modulus/text/features/configuration.html)."
+    "More information on the available configurations can be found in [Modulus Configuration](https://docs.nvidia.com/deeplearning/modulus/modulus-sym/user_guide/features/configuration.html)."
    ]
   },
   {
@@ -787,7 +787,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
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