Introduction

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.

logos

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

Introduction

Contents

Introduction#

Expected learning outcomes#