Updated jupter notebooks

Author: Abhijnan03

books/.ipynb_checkpoints/doe-checkpoint.ipynb

@@ -6,12 +6,12 @@
    "source": [
     "#  Sampling Plans\n",
     "\n",
-    "This section provides implementation for the concepts covered in the class for sampling plans. Following topics are covered in this section:\n",
+    "This section provides implementation for concepts related to sampling plans. Following topics are covered in this section:\n",
     "\n",
     "1. Full Factorial Sampling\n",
     "2. Latin Hypercube Sampling\n",
     "\n",
-    "> **_NOTE:_** You need to install [smt](https://smt.readthedocs.io/en/latest/) which is a python-based surrogate modeling toolbox. It provides various surrogate modeling techniques, along with different sampling plans. Activate the environment you created for this class in anaconda prompt and install smt using `pip install smt`.\n",
+    "> **_NOTE:_** You need to install [smt](https://smt.readthedocs.io/en/latest/) which is a python-based surrogate modeling toolbox. It provides various surrogate modeling techniques, along with different sampling plans. Activate the environment you created in anaconda prompt and install smt using `pip install smt`.\n",
     "\n",
     "Below block of code imports required packages:"
    ]
@@ -291,7 +291,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "sm",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -305,9 +305,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.0"
+   "version": "3.9.19"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }

books/.ipynb_checkpoints/prob_form-checkpoint.ipynb

@@ -6,7 +6,7 @@
    "source": [
     "# Global Optimization\n",
     "\n",
-    "This section provides implementation for the concepts covered in the class for global optimization.\n",
+    "This section provides implementation for concepts related to global optimization.\n",
     "Each subsection contains various code blocks which provide python implementation for the concept. \n",
     "You are expected to create an empty jupyter notebook, and paste the code in the notebook and run it. \n",
     "This will teach you how to create notebooks from scratch and prepare you for workbook and homework \n",
@@ -423,7 +423,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "sm",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -437,9 +437,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.18"
+   "version": "3.9.19"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }

books/doe.ipynb

@@ -8,7 +8,7 @@
     "\n",
     "In this section, Particle Swarm Optimization (PSO) algorithm is demonstrated. PSO is a population-based stochastic optimization algorithm inspired by social behavior. The algorithm was originally proposed by Kennedy and Eberhart in 1995. The algorithm is based on a population of particles that move around in search space and update their position and velocity based on locally and globally best-found solutions, refer lecture notes for more details.\n",
     "\n",
-    "> **_NOTE:_** You need to install [pymoo](https://pymoo.org/index.html) which provides single- and multi-objective global optimization algorithms. Activate the environment you created for this class in anaconda prompt and install pymoo using `pip install pymoo`.\n",
+    "> **_NOTE:_** You need to install [pymoo](https://pymoo.org/index.html) which provides single- and multi-objective global optimization algorithms. Activate the environment you created in anaconda prompt and install pymoo using `pip install pymoo`.\n",
     "\n",
     "[Jones function](intro:jones-function) will be used to demonstrate the method. Following block imports all required packages:"
    ]
@@ -335,7 +335,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "sm",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -349,9 +349,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.18"
+   "version": "3.9.19"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }

books/global_opt/.ipynb_checkpoints/de-checkpoint.ipynb

@@ -1,5 +1,5 @@
 # Introduction
-This jupyter-book provides python implementation for various concepts and algorithms related to **Surrogate Methods**. The jupyter-book has been created by the **Computational Design (CODE) Laboratory** led by **Prof. Leifur Leifsson** at **Purdue University**. This jupyter-book is intended to be used as a starting point for new users of surrogate methods to implement the concepts and algorithms for their own work. The creation of this jupyter-book is funded partly by the National Science Foundation (NSF) award number 2223732. 
+This jupyter-book provides python implementation for various concepts and algorithms related to **surrogate modeling** and **surrogate-based optimization**. The jupyter-book has been created by the **Computational Design (CODE) Laboratory** led by **Prof. Leifur Leifsson** at **Purdue University**. This jupyter-book is intended to be used as a starting point for new users of surrogate methods to implement the concepts and algorithms for their own work uisng open source codes. The creation of this jupyter-book is funded partly by the National Science Foundation (NSF) award number 2223732. 
 
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