Simulation with Python: Develop Simulation and Modeling in Natural Sciences, Engineering, and Social
Rongpeng / Nakano Li
Résumé
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.
Chapter 6: Queuing System: How Stock Trades are Made [30]Description: As the first example in the business world, concepts in queuing systems are introduced and the simulation using basic data structures like queue and deque will be carried out.Topics to be covered: 1. Basic data structures in Python2. Microstructure of trading3. Simulating trading Chapter 7: Rock, Scissor and Paper: Multi-Agent Simulation [30]Description: Sometimes we want to simulate a system with multiple agents acting on their own behalf. In this chapter, we are going to run a multi-agent simulation and test the performance of different competing strategies in such a scenario.Topics to be covered: 1. Characteristics of multi-agent system2. Baseline strategies3. Analyzing nontrivial strategies
Chapter 8: Matthew Effect and Tax Policy: Why the Rich Keeps Getting Richer[30]Description: Differential equation is an important field of study that governs a big group of phenomena. In this chapter, we are going to study it with a very relevant topic: wealth distribution in modern society. Topics to be covered: 1. Introduction of differential equations2. Matthew effect and ROI3. How tax policy can gauge social wealth distribution Chapter 9: Misinformation Spreading: Simulation on a Graph (Centrality, Networkx)[30]Description: Network simulation is another important domain. Nowadays social media like Twitter, Facebook and reddit can be easily modelled as a network. We will cover a simple simulation to study how misinformation can spread in a network and how we can fight against it.Topics to be covered: 1. Concepts of a network
2. Simulate misinformation spreading in a directed network3. How to fight misinformation (or suppress freedom of expression)
Chapter 10: Simulated Annealing and Genetic Algorithm [30]Description: There are two simulation algorithms widely used in research and industry that mimic natural phenomena. We are going to use them to solve two real world problems and explain the origin of their power.Topics to be covered: 4. Simulated Annealing Basics5. Use Simulated Annealing to solve an optimization problem6. Genetic Algorithm7. Use Genetic algorithm to solve an optimization problem
Aiichiro Nakano is a Professor of Computer Science with joint appointments in Physics & Astronomy, Chemical Engineering & Materials Science, Biological Sciences, and at the Collaboratory for Advanced Computing and Simulations at the University of Southern California. He received a PhD in physics from the University of Tokyo, Japan, in 1989. He has authored more than 360 refereed articles in the areas of scalable scientific algorithms, massive data visualization and analysis, and computational materials science.
Caractéristiques techniques
PAPIER | |
Éditeur(s) | Apress |
Auteur(s) | Rongpeng / Nakano Li |
Parution | 23/08/2022 |
Nb. de pages | 166 |
EAN13 | 9781484281840 |
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