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9780750338417

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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

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 -- John J. Shea

Author Information

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.

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