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.

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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