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Kerson Huang
人物简介:
Introduction to Statistical Physics, Second Edition书籍相关信息
- ISBN:9781420079029
- 作者:黄克孙 / Kerson Huang
- 出版社:Chapman and Hall/CRC
- 出版时间:2009-9-21
- 页数:333
- 价格:USD 71.95
- 纸张:暂无纸张
- 装帧:Hardcover
- 开本:暂无开本
- 语言:暂无语言
- 适合人群:academic researchers, physics students, engineers, data scientists, anyone interested in the interdisciplinary applications of physics and mathematics
- TAG:Mathematical Modeling / Statistical Inference / Thermodynamics / Statistical Mechanics / Classical Physics / Complex Systems / Quantum Physics / Phase Transitions
- 豆瓣评分:暂无豆瓣评分
- 更新时间:2025-05-17 04:28:06
内容简介:
Written by a world-renowned theoretical physicist, Introduction to Statistical Physics, Second Edition clarifies the properties of matter collectively in terms of the physical laws governing atomic motion. This second edition expands upon the original to include many additional exercises and more pedagogically oriented discussions that fully explain the concepts and applications. The book first covers the classical ensembles of statistical mechanics and stochastic processes, including Brownian motion, probability theory, and the Fokker--Planck and Langevin equations. To illustrate the use of statistical methods beyond the theory of matter, the author discusses entropy in information theory, Brownian motion in the stock market, and the Monte Carlo method in computer simulations. The next several chapters emphasize the difference between quantum mechanics and classical mechanics--the quantum phase. Applications covered include Fermi statistics and semiconductors and Bose statistics and Bose--Einstein condensation. The book concludes with advanced topics, focusing on the Ginsburg--Landau theory of the order parameter and the special kind of quantum order found in superfluidity and superconductivity. Assuming some background knowledge of classical and quantum physics, this textbook thoroughly familiarizes advanced undergraduate students with the different aspects of statistical physics. This updated edition continues to provide the tools needed to understand and work with random processes.