SIMULATION WITH PYTHON : develop simulation and modeling in natural sciences, engineering, and... social sciences

This book is a small gift to a younger me, probably in high school or even earlier. This book is by no means written for seasoned researchers or professionals. It should be treated as the first bite of ice cream which makes you want more.This book contains several scientific simulation topics, ranging from physics, biology, and even finance. The approach is very gentle and newcomer friendly. I tried to remove the majority of the complexity that I would learn with the knowledge and scientific training I already had. Instead, I did my best to keep the most important essence in each topic. The persona in my mind is a young and curious student who just got the first computer and learned some basic programming, probably from the older brother. This student, pictured as a younger me, would be able to follow the content of this book without any difficulty and get amazed by the beautiful visualizations and scientific conclusions.Each topic in this book is rather independent. According to the level of technical difficulty and required background knowledge, I categorize the chapters into three groups. Readers can start with any chapter. Easy: Chapter 1: Calculating Pi with Monte Carlo Simulation Chapter 4: Balls in a 2-D Box, a Simple Physics EngineMedium: Chapter 2: Markov Chain, a Peek into the Future Chapter 3: Multi-armed Bandits, Probability Simulation, and Bayesian Statistics Chapter 7: Rock, Scissors, and Paper: Multi-agent Simulation Chapter 8: Disease Spreading, Simulating COVID-19 Outbreak Chapter 9: Misinformation Spreading and Simulations on a GraphHard: Chapter 5: Percolation, Threshold, and Phase Change Chapter 6: Queuing System: How Stock Trades Are MadeI hope you enjoy this book as much as I do. 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 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 Learn Use Python and numerical computation to demonstrate the power of simulation Choose a paradigm to run a simulation Draw statistical insights from numerical experiments Know 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. 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. You will: Use Python and numerical computation to demonstrate the power of simulation Choose a paradigm to run a simulation Draw statistical insights from numerical experiments Know how simulation is used to solve real-world problems