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arXiv 0901.2496cond-mat.stat-mech
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计算统计物理四讲

Four lectures on computational statistical physics

Werner Krauth

摘要:

在2008年Les Houches暑期学校的讲座中,我讨论了计算统计物理学的核心概念,我觉得这对学校的跨文化观众来说是容易理解的。我首先讨论了抽样,这是蒙特卡罗方法的核心。我特别强调了完美采样的概念,它提供了传统的直接和马尔可夫链采样方法的综合。第二堂课是关于经典硬球系统的,它阐明了统计力学的基础,但也说明了即使是最新的模拟也会遇到的奇怪困难。然后,在第三讲中,我继续讲量子蒙特卡罗方法,这是玻色子系统中许多现代工作的基础。量子蒙特卡罗是一个复杂的课题。然而,我们可以在简化的情况下讨论它(单粒子自由传播子、理想玻色子),并且只用二三十行代码就可以写出这两种情况下的直接采样算法。这些理想化的算法说明了该领域的许多关键思想。第四讲试图说明近年来在伊辛模型模拟中实现的物理学统一性的各个方面。更多关于我在Les Houches讨论的细节,以及我在这里写的(并重新整理),可以在我的书中找到,“统计力学:算法和计算”,以及最近的论文。计算机程序可在本书的网站www.smac.lps.ens.fr上下载和阅读。

英文摘要:

In my lectures at the Les Houches Summer School 2008, I discussed central concepts of computational statistical physics, which I felt would be accessible to the very cross-cultural audience at the school. I started with a discussion of sampling, which lies at the heart of the Monte Carlo approach. I specially emphasized the concept of perfect sampling, which offers a synthesis of the traditional direct and Markov-chain sampling approaches. The second lecture concerned classical hard-sphere systems, which illuminate the foundations of statistical mechanics, but also illustrate the curious difficulties that beset even the most recent simulations. I then moved on, in the third lecture, to quantum Monte Carlo methods, that underly much of the modern work in bosonic systems. Quantum Monte Carlo is an intricate subject. Yet one can discuss it in simplified settings (the single-particle free propagator, ideal bosons) and write direct-sampling algorithms for the two cases in two or three dozen lines of code only. These idealized algorithms illustrate many of the crucial ideas in the field. The fourth lecture attempted to illustrate aspects of the unity of physics as realized in the Ising model simulations of recent years. More details on what I discussed in Les Houches, and wrote up (and somewhat rearranged) here, can be found in my book, "Statistical Mechanics: Algorithms and Computations", as well as in recent papers. Computer programs are available for download and perusal at the book's web site www.smac.lps.ens.fr.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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