# Introduction#

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 using open source codes. The creation of this jupyter-book is funded partly by the National Science Foundation (NSF) award number 2223732.

## Expected learning outcomes#

After going through this jupyter-book, the reader will:

Have gained basic knowledge of surrogate methods and algorithms

Have gained an understanding of the computational challenges of surrogate methods

Be able to implement basic algorithms of surrogate methods in Python

Be able to use surrogate algorithms to solve basic engineering modeling and design optimization problems