Simulation of Complex Systems: (IOP ebooks)

Web Copy  This book deals with the most fundamental and essential techniques to simulate complex systems, from the dynamics of molecules to the spreading of diseases, from optimization using ant colonies to the simulation of the Game of Life. Several natural systems found in physics, biology and engineering can be considered complex systems, because their behaviour is not easily predictable and is the result of complex interactions among their constituents. Examples of complex systems are a cell with its organelles, an organ, the human brain, social networks, transportation and communication systems, the stock market, ecosystems, systems with prey and predators, a swarm of bees. There are several specialized books focusing on different simulation methods, but there is not one fully devoted to complex systems. The “bottom-up” approach is innovative and allows the reader to conduct numerical experiments to explore the system’s behaviour. Key Features 1.      Composed of self-contained, independent chapters 2.      Illustrates simulation techniques in a broad range of fields from physics and biology to engineering, social science and economics 3.      Provides a hands-on approach with guided exercises 4.      Covers the fundamental numerical techniques in complex systems 5.      Ideal for self-study 6.      Contains supplementary example codes and video tutorials

Table of Contents:
Contents 1 Molecular Dynamics 2 Ising model 3 Forest Fires 4 The Game of Life 5 Brownian Dynamics 6 Anomalous Diffusion 7 Multiplicative Noise 8 The Vicsek Model 9 Living Crystals 10 Sensory Delay 11 Disease Spreading 12 Network Models 13 Evolutionary Games 14 Ecosystems 15 Ant-colony Optimization 16 The Sugarscape

About the Author :
Author Bios Giovanni Volpe Giovanni Volpe is a Professor at the Physics Department of the University of Gothenburg University, where he leads the Soft Matter Lab. His research interests include soft matter, active matter, optical trapping and manipulation, statistical mechanics, brain connectivity, and machine learning. He has authored more than 100 articles and reviews on soft matter, statistical physics, optics, physics of complex systems, brain network analysis, and machine learning. He co-authored the book “Optical Tweezers: Principles and Applications” (Cambridge University Press, 2015). He has developed several software packages (Optical Tweezers Software, Braph – Brain Analysis Using Graph Theory, DeepTrack, DeepCalib).   Agnese Callegari Agnese Callegari is a researcher at the Physics Department of Gothenburg University. Her research interests are optical trapping and manipulation, statistical physics, soft matter, active matter. She has authored 14 publications, and she has extensive experience in numerical simulations. She has been teaching basic physics courses for scientists and engineers.   Aykut Argun Aykut Argun is a PhD student in Physics at Gothenburg University. His research interests are optical trapping and manipulation, statistical physics, soft matter, active matter, machine learning technique applied to experimental data Analysis. He has authored 8 publications, and he has served several years as a teaching assistant in courses like “Simulation of complex systems”, “Optical trapping”, “Statistical physics.” He has a solid experience in teaching and explaining physics to high school, undergraduate and graduate students.

Review :
Modeling complex systems can help to predict outcomes that cannot be easily predicted. This book uses numerical simulations to understand complex systems. It explains numerical simulation techniques most often used to approach a variety of complex systems that are of fundamental importance in physics, biology, engineering, social sciences, and economics. In addition to the use of numerical simulations for modeling and understanding phenomena for applications, numerical simulations are ideal tools for hands-on experience with complex systems. Each chapter is an independent topic and does not require reading previous chapters to understand the material. Each chapter includes an introduction and the motivation for the topic, a description of relevant numerical approaches to the problem at hand with guided exercises, a list of references for further study, and practice problems. With the help of this book, it should be possible for readers to reach a level of proficiency, sufficient to perform these methods and create models for their applications. The book begins with some basic topics that describe numerical simulation techniques that are of fundamental importance in physics and engineering (e.g., molecular dynamics, passive and active Brownian dynamics, anomalous diffusion, and multiplicative noise) and continues with more specialized topics in biology, engineering, and the social sciences. This book is written at a master's degree and graduate student level, making it ideal for a course in modeling and simulation of complex systems as well as for self study. John J. Shea, IEEE Magazine, December 2022