Computational Statistical Physics

This book provides a comprehensive and accessible introduction to computational statistical physics, covering both equilibrium and non-equilibrium theories and their applications in fields such as percolation, random walks, and machine learning. It is suitable for graduate-level courses and includes worked examples and problems for students to test their understanding.

Format: Hardback
Length: 268 pages
Publication date: 26 August 2021
Publisher: Cambridge University Press

Computational statistical physics is a rapidly growing field that encompasses the theoretical foundations of equilibrium and non-equilibrium statistical physics, as well as modern computational applications in areas such as percolation, random walks, magnetic systems, machine learning dynamics, and spreading processes on complex networks. This comprehensive book provides a detailed and pedagogical account of the subject, covering both the theoretical underpinnings and practical applications.

The theoretical foundations of equilibrium and non-equilibrium statistical physics are explored in depth, covering topics such as probability distributions, thermodynamics, and statistical mechanics. The authors provide a clear and concise explanation of these concepts, using mathematical techniques and computational methods to illustrate their ideas.

In addition to the theoretical aspects, the book also discusses modern computational applications in various fields. Percolation, for example, is a process in which particles or molecules spread through a network, and computational statistical physics has played a crucial role in understanding its behavior. Random walks, another important application, are studied to understand the dynamics of particles moving on a lattice or in a random environment. Magnetic systems, including spin glasses and magnetic fluids, are investigated to understand their properties and behavior. Machine learning dynamics, which involves analyzing the behavior of complex systems using machine learning algorithms, is also discussed.

Furthermore, the book includes a detailed discussion of molecular dynamics simulations, a topic of great importance in biophysics and physical chemistry. Molecular dynamics simulations allow researchers to study the behavior of molecules and their interactions at the atomic level, providing insights into biological processes and material properties.

The accessible and self-contained approach adopted by the authors makes this book suitable for teaching courses at the graduate level. Numerous worked examples and end-of-chapter problems are provided to allow students to test their progress and understanding. The book also includes a comprehensive bibliography and an index, making it a valuable resource for researchers and students alike.

In conclusion, Computational Statistical Physics: Theoretical Foundations and Modern Applications is a comprehensive and authoritative book that provides a thorough understanding of the rapidly growing field of computational statistical physics. It is an essential resource for researchers, students, and anyone interested in exploring the theoretical foundations and practical applications of statistical physics.

Weight: 760g
Dimension: 208 x 259 x 24 (mm)
ISBN-13: 9781108841429