Modeling and Simulation in Python von Allen B. Downey

Kategorie: Bücher
Seiten / Format: 264 S
Erscheinungsjahr: 2022
Verlag: No Starch Press
Sprache: Englisch
ISBN: 9781718502161

Introduction<br>Part I: Discrete Systems<br>Chapter 1:Modeling<br>Chapter 2Bike Share System<br>Chapter 3:Iteration<br>Chapter 4:Sweeping Parameters<br>Chapter 5:World Population<br>Chapter 6:Proportional Growth<br>Chapter 7:Limits to Growth<br>Chapter 8:Projecting Population Growth<br>Chapter 9:Analysis of Population Growth<br>Chapter 10:Case Studies Part 1<br>Part II: First Order Systems<br>Chapter 11:Epidemiology<br>Chapter 12:Modeling Vaccination<br>Chapter 13:Sweeping Parameters<br>Chapter 14:Nondimensionalization<br>Chapter 15:Cooling Coffee<br>Chapter 16:Adding Milk<br>Chapter 17:Pharmacokinetics<br>Chapter 18:Glucose and Insulin<br>Chapter 19:Case Studies Part 2<br>Part III: Second Order Systems<br>Chapter 20:Pennies<br>Chapter 21:Drag<br>Chapter 22:Baseball<br>Chapter 23:Optimization<br>Chapter 24:Rotation<br>Chapter 25:Torque<br>Chapter 26:Case Studies Part 3<br>Appendix A Under the Hood"Modeling and Simulation in Pythonis an introduction to physical modeling using a computational approach . . . Taking a computational approach makes it possible to work with more realistic models than what you typically see in a first-year physics class, with the option to include features like friction and drag."<br>Python KitchenModeling and Simulation in Pythonteaches readers how to analyze real-world scenarios using the Python programming language, requiring no more than a background in high school math.<br><br>Modeling and Simulation in Pythonis a thorough but easy-to-follow introduction to physical modeling that is, the art of describing and simulating real-world systems. Readers are guided through modeling things like world population growth, infectious disease, bungee jumping, baseball flight trajectories, celestial mechanics, and more while simultaneously developing a strong understanding of fundamental programming concepts like loops, vectors, and functions.<br><br>Clear and concise, with a focus on learning by doing, the author spares the reader abstract, theoretical complexities and gets right to hands-on examples that show how to produce useful models and simulations.2USAllen B. Downeyis a Boston-area professor of Computer Science at Olin College and the author of a series of open-source textbooks related to software and data science, includingThink Python,Think Bayes, andThink Complexity. His blog, Probably Overthinking It, features articles on Bayesian probability and statistics. Downey holds a Ph.D in computer science from U.C. Berkeley, and M.S. and B.S. degrees from MIT. 

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