Simulation with Python: Develop Simulation and Modeling in Natural Sciences, Engineering, and Social Sciences

Description

Understand the theory and implementation of simulation. This book covers simulation topics from a scenario-driven approach using Python and rich visualizations and tabulations.  The book discusses simulation used in the natural and social sciences and with simulations taken from the top algorithms used in the industry today. The authors use an engaging approach that mixes mathematics and programming experiments with beginning-intermediate level Python code to create an immersive learning experience that is cohesive and integrated.  After reading this book, you will have an understanding of simulation used in natural sciences, engineering, and social sciences using Python. What You'll LearnUse Python and numerical computation to demonstrate the power of simulationChoose a paradigm to run a simulationDraw statistical insights from numerical experimentsKnow how simulation is used to solve real-world problems  Who This Book Is For Entry-level to mid-level Python developers from various backgrounds, including backend developers, academic research programmers, data scientists, and machine learning engineers. The book is also useful to high school students and college undergraduates and graduates with STEM backgrounds. Table of Contents About the Authors About the Technical Reviewer Acknowledgments Introduction Chapter 1: Calculating Pi with Monte Carlo Simulation Background The Wise Persons’ Competition Estimating Pi by Sprinkling Grains Exercise Contain the Goat! What Randomness? Exercise Summary Chapter 2: Markov Chain, a Peek into the Future Weather Forecasting Eigenstates of Markov Chains Exercise Markov Chain Applications A Random Walk That Has an End Sonnet Written by Drunk Shakespeare Exercise Summary Chapter 3: Multi-armed Bandits, Probability Simulation, and Bayesian Statistics Random Pick and Naive Greedy Approach Greedy-Epsilon: Greedy but Not Always An Improved Greedy-Epsilon Algorithm Exercise The Bayesian Way, a Primer on Bayesian Statistics Exercise Summary Chapter 4: Balls in a 2-D Box, a Simple Physics Engine One Ball in a 2-D Box Physics Law of Motion Collision Detection Exercise Multiple Balls in a 2-D Box Update of Positions and Velocity upon Collision Collision Detection in Multiple-Ball Scenario Exercise Summary Chapter 5: Percolation, Threshold, and Phase Change Problem Introduction Percolation and the Critical Probability An Analytical Solution for the 1-D Case A Simulation for the 2-D Case Exercise Another Interesting Statistic in 2-D Grid Percolation Exercise Summary Chapter 6: Queuing System: How Stock Trades Are Made Trading Process Fundamentals The Order Book Create the Interfaces and Determine the Data Schema Implement Order Book Logic Hook the Bots and Engine Together Exercises and Extension Ideas Multiple Bots An Informed Bot Order Book Visualization Order Cancellation Support Stop Orders Support Summary Chapter 7: Rock, Scissors, and Paper: Multi-agent Simulation Community Formation on a Street Exercise Original Schelling Model Three Groups How to Win a Global Rock, Paper, and Scissors Contest Exercise Summary Chapter 8: Disease Spreading, Simulating COVID-19 Outbreak Simplifying the Real World The SI Model Exercise The SIR Model Exercise Summary Chapter 9: Misinformation Spreading and Simulations on a Graph Model the Social Network Simulate Misinformation Spreading Simple Cases Misinformation Spreading on Different Networks Exercise Summary Index