Modeling and Simulation in Python: An Introduction for Scientists and Engineers

Description

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. This book is about dynamical systems, that is, things that change over time. The first example we’ll look at is a penny falling from the Empire State Building, where the thing that’s changing is the position of the penny in space. Other examples include a cup of coffee, where temperature changes over time, and glucose in the human bloodstream, where concentration changes over time. We will define models, which are simplifications intended to include the most important elements of the real world and leave out the least important, and we will write Python programs that simulate these models. We will use models and simulations to do three kinds of work: predicting how a system will behave, explaining why it behaves as it does, and designing systems to behave the way we want. Acknowledgments Introduction PART I: DISCRETE SYSTEMS Chapter 1: Introduction to Modeling Chapter 2: Modeling a Bike Share System Chapter 3: Iterative Modeling Chapter 4: Parameters and Metrics Chapter 5: Building a Population Model Chapter 6: Iterating the Population Model Chapter 7: Limits to Growth Chapter 8: Projecting into the Future Chapter 9: Analysis and Symbolic Computation Chapter 10: Case Studies Part I PART II: FIRST-ORDER SYSTEMS Chapter 11: Epidemiology and SIR Models Chapter 12: Quantifying Interventions Chapter 13: Sweeping Parameters Chapter 14: Nondimensionalization Chapter 15: Thermal Systems Chapter 16: Solving the Coffee Problem Chapter 17: Modeling Blood Sugar Chapter 18: Implementing the Minimal Model Chapter 19: Case Studies Part II PART III: SECOND-ORDER SYSTEMS Chapter 20: The Falling Penny Revisited Chapter 21: Drag Chapter 22: Two-Dimensional Motion Chapter 23: Optimization Chapter 24: Rotation Chapter 25: Torque Chapter 26: Case Studies Part III Appendix: Under the Hood Index