R. K. Pathria 和 Paul D. Beale 合著 “统计力学” 内容简介 本书系根据英国珀珈芒出版社(Pergamon Press) (或学术出版社, Academic Press ) 出版,系 R. K. Pathria 和 Paul D. Beale 合著的 “统计力学”一书2011年第三版,由方锦清和戴越译出,中国教育出版社即将出版. 全书共十六章。 首先阐述了经典统计力学理论,包括热力学的统计基础和系综理论的基本原理, 讨论了微正则系综、正则系综和巨正则系综 。随后, 将系综概念和量子力学概念相结合, 详细讲述了量子统计力学,并将其表述形式具体应用于 遵循玻色-爱因斯坦统计法和费米-狄拉克统计法等系统 。同时,讨论了统计力学的若干其它重要课题:相互作用系统的统计力学主要方法(集团展开法、赝势法和量子化场方法);相变理论(各种模型的严格解、重正化群方法);早期宇宙的热力学;非平衡态统计力学和涨落理论,以及 蒙特卡罗和分子动力学模拟方法 等,还有若干相关附录和练习题。 本书可作为物理、化学和交叉科学(如复杂性科学、网络科学等)专业的研究生教材,亦可供相关专业的高年级本科生、科研人员和教师参考。
"网络科学与统计物理方法"内容简介 (按语:该书即将出版,应有关网友要求,在此作一简介) 这是一本关于网络科学和非平衡统计物理最新发展的学术论著,由三大部分共 30 章构成,集作者及国内外该领域的主要研究成果,涉及网络科学和统计物理的重要论题,两大主题各有千秋、相互交融,范围从天到地,从宏观到介观、微观,乃之宇观,跨度之大实属少见,揭示了如此大跨度的不同领域存在着内在联系,具有相当的普适性和内在的逻辑性。这使得本书不仅能够分别提供一些明确的研究结果,而且具有前瞻性、可导性、交叉性和应用性。全书力求尽可能深入浅出,尽量使读者在具有普通大学本科的基础上就可读懂本书主要内容。同时,全书富有特色和独创性,有许多涉及学科前沿的内容还是首次发表。所以本书更适宜于高年级大学生和研究生作为网络科学和统计物理的教材或参考著作,并不失为一本供交叉科学工作者参考的高水平专著。 Abstract This is an academic book about the latest developments in the network science and the non-equilibrium statistical physics. A total of 30 chapters by the three major compositions, gathered main achievements from authors, domestic and abroad in this field, involving important topics for the network science and the statistical physics; two major themes show respectively advantages, interacting with each other, ranging from sky to land, from the macro to the mesoscopic or microscopic world, until macrocosm. The large span is rare; revealing such a large span of different areas is intrinsically linked, with considerable universal and inherent logic. This makes the manuscript not only provide some definite findings but with forward-looking, leading, crossing and application. The book seeks to easier way as much as possible, as far as possible with readers on the basis of ordinary undergraduate can read the main content of the book. Meanwhile, the book is distinctive and original, there are many disciplines involved in frontier content first published. So the books is very suitable for senior undergraduate or graduate as a textbook or reference for network science and statistical physics, and also well be a reference for the cross-scientists as high level of monograph.