
This book discusses the computational approach in modern statistical physics in a clear and accessible way and demonstrates its close relation to other approaches in theoretical physics. Individual chapters focus on subjects as diverse as the hard sphere liquid, classical spin models, single quantum particles and Bose-Einstein condensation. Contained within the chapters are in-depth discussions of algorithms, ranging from basic enumeration methods to modern Monte Carlo techniques. The emphasis is on orientation, with discussion of implementation details kept to a minimum. Illustrations, tables and concise printed algorithms convey key information, making the material very accessible. The book is completely self-contained and graphs and tables can readily be reproduced, requiring minimal computer code. Most sections begin at an elementary level and lead on to the rich and difficult problems of contemporary computational and statistical physics. The book will be of interest to a wide range of students, teachers and researchers in physics and the neighbouring sciences. An accompanying CD allows incorporation of the book's content (illustrations, tables, schematic programs) into the reader's own presentations.
This text investigates the integration of computational algorithms into the study of modern statistical physics to bridge the gap between theoretical models and numerical simulation. Werner Krauth, a researcher in computational physics, utilizes a pedagogical framework that prioritizes conceptual orientation over exhaustive coding implementation. By focusing on the relationship between theoretical physics and algorithmic application, the book provides a structured approach to solving complex physical problems.
What You Will Find
Scope Limits
Experts and educators recognize this work as a foundational text for students and researchers seeking to understand the intersection of computation and statistical physics. Readers frequently note the clarity of the prose and the effectiveness of the provided illustrations in conveying complex algorithmic concepts.
Page Count:
342
Publication Date:
2006-01-01
Publisher:
OUP Oxford
ISBN-10:
0191523321
ISBN-13:
9780191523328
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