Modeling and Simulation in Python
| Verlag | No Starch Press |
| Auflage | 2022 |
| Seiten | 264 |
| Format | 18,8 x 1,6 x 23,3 cm |
| Gewicht | 530 g |
| Artikeltyp | Englisches Buch |
| EAN | 9781718502161 |
| Bestell-Nr | 71850216UA |
Modeling and Simulation in Python teaches readers how to analyze real-world scenarios using the Python programming language, requiring no more than a background in high school math.
Modeling and Simulation in Python is 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.
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.
Inhaltsverzeichnis:
Introduction
Part I: Discrete Systems
Chapter 1: Modeling
Chapter 2 Bike Share System
Chapter 3: Iteration
Chapter 4: Sweeping Parameters
Chapter 5: World Population
Chapter 6: Proportional Growth
Chapter 7: Limits to Growth
Chapter 8: Projecting Population Growth
Chapter 9: Analysis of Population Growth
Chapter 10: Case Studies Part 1
Part II: First Order Systems
Chapter 11: Epidemiology
Chapter 12: Modeling Vaccination
Chapter 13: Sweeping Parameters
Chapter 14: Nondimensionalization
Chapter 15: Cooling Coffee
Chapter 16: Adding Milk
Chapter 17: Pharmacokinetics
Chapter 18: Glucose and Insulin
Chapter 19: Case Studies Part 2
Part III: Second Order Systems
Chapter 20: Pennies
Chapter 21: Drag
Chapter 22: Baseball
Chapter 23: Optimization
Chapter 24: Rotation
Chapter 25: Torque
Chapter 26: Case Studies Part 3
Appendix A Under the Hood
Rezension:
"Modeling and Simulation in Python is 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."
Python Kitchen
