Modeling and Simulation in Python

by Jason M. Kinser

Overview

The use of Python as a powerful computational tool is expanding with great strides. Python is a language which is easy to use, and the libraries of tools provides it with efficient versatility. As the tools continue to expand, users can create insightful models and simulations. While the tools offer an easy method to create a pipeline, such constructions are not guaranteed to provide correct results. A lot of things can go wrong when building a simulation - deviously so. Users need to understand more than just how to build a process pipeline.

Modeling and Simulation in Python introduces fundamental computational modeling techniques that are used in a variety of science and engineering disciplines. It emphasizes algorithmic thinking skills using different computational environments, and includes a number of interesting examples, including Shakespeare, movie databases, virus spread, and Chess.

Key Features:

• Several theories and applications are provided, each with working Python scripts.
• All Python functions written for this book are archived on GitHub.

Readers do not have to be Python experts, but a working knowledge of the language is required. Students who want to know more about the foundations of modeling and simulation will find this an educational and foundational resource.

Product Details

ISBN-13:	9781032128702
Publisher:	CRC Press
Publication date:	05/27/2024
Pages:	332
Product dimensions:	7.00(w) x 10.00(h) x (d)

About the Author

Jason M. Kinser is Chair of the Department of Computational and Data Sciences at George Mason University. Dr. Kinser has taught more than 30 different courses at Mason in several departments including Computational & Data Sciences, Physics, Bioinformatics, Forensics, and even Biology. His research interests include analysis of images, fusion of image content non-image data, and education.

Table of Contents

Preface

Software

1. Introduction

2. Random Values

3. Application of Random Values 

4. The Monte Carlo Method

5. Modeling Self-Organization 

6. Hidden Markov Models

7. Identification of Start Codons

8. HMM Application in Baseball

9. Hidden Shakespeare Model

10. Connected Data

11. Gene Expression Arrays

12. Simultaneous Equations

13. Simulations of Motion

14. Oscillations

15. Coupled Differential Equations

16. Extraordinary Number of Solutions

17. Agent Based Modeling - Virus Spread

18. Chess

Bibliography

Index