4.MIT教材《Introduction to Statistical Physics》评介研究
材料中的热力学与动力学1
17
The state of a System at Equilibrium: -Defined by the collection of all macroscopic properties that are described by State variables (p, n, T, V, …)
or
∆U=q+w
− ������������= ������������
7
2th Law:
Define Entropy: - Puts restrictions on useful conversion of q to w - Follows from observation of a directionality to natural or spontaneous processes - Provides a set of principles for - determining the direction of spontaneous change - determining equilibrium state of system
11
3th Law:
Corollary:
It’s impossible to decrease the temperature of any system to T=0K in a finite number of steps.
12
These laws are Universally Valid, they cannot be circumvented.
-For a one-component System, all that is required is “n” and 2 variables. All other properties then follow.
MIT-SCIENCE-Lectures-intro_sts_p12003
INTRODUCTION TO STATISTICS FOR POLITICAL SCIENCE:1.IntroductionStephen AnsolabehereDepartment of Political ScienceMassachusetts Institute of TechnologyFall,20031.IntroductionStatistical tools are essential for social scientists.Basic concepts of statistics,especially randomness and averaging,provide the foundations for measuring concepts,designing stud-ies,estimating quantities of interest,and testing theories and conjectures.People are not always good statisticians.It is hard to maintain discipline in observing the world.W e often learn from what is convenient-a violation of random sampling.We often do not calculate averages well.To learn these concepts with the depth associated with a graduate education-where you will have the facility to use these concepts in your own research and possibly contribute to the development of statistical models that others may use-requires some mathematics.We will use,repeatedly,three sorts of functions-polynomials(especially quadratics),exponentials, and logarithms.We will also use summation,as that is necessary for the calculation of averages,and summation comes in two forms-discrete and continuous(or integration).We will use di®erencing and di®erentiation(the continuous version of di®erencing).Finally,we will use probability a special branch of mathematics designed to study uncertainty.This course is designed to be a self-contained introduction not only to the concepts but also to the tools of statistics for social sciences.At the beginning of this course I will review the basic mathematical tools used in statistics.As a result we will study calculus and probability theory.Much of the basic mathematics that social scientists use in statistical analyses and in formal modeling comes from the calculus,especially limits,derivatives,and integrals.Probability provides a theory of uncertainty,and is thus the essential tool of statistics.1.Two core ideas in statistics.A.AveragingStatistics involves studying the frequencies of events and behaviors.W e assume that every event has its own likelihood of occurence,such as the likelihoodof the birth of a boy or girl.The long-run average is a measure of that frequency.One important law of statistics is the Law of Large Numbers.If we observe the repetition of a certain trial or experiment or event,such as birth,the long-run frequency with which one outcome or another happens,such as a boy or a girl is born,is extremely close to the true frequency of that outcome.A second important law of statistics is the Central Limit Theorem,which states that the frequency of possible outcomes of a sum of variables follows a bell-shaped(or normal)curve.We will make both of these laws more precise later in the course.B.RandomnessProbability is the study of randomness and chance.The systematic study of probability emerged as an important mathematical subject of study in the18th Century.In the late 18th and19th Centuries the application of probability spread beyond games of chance to the study of physical and social behavior.And in the20th Century researchers realized that one could use randomness to increase the e±ciency with which we learn.That is perhaps the most surprising and counter intuitive aspect of statistics{randomness is useful.Two core applications of this idea are(1)random sample surveys and(2)randomized experiments.1.Random Sample Surveys:How can we learn about100million people with just1000?Random sample surveys are the most widely used tool for measuring quantities of interest in all of the social sciences.Nearly all government data are collected using random sample surveys-including measures of the economic and social conditions of the nation,ranging from crime to in°ation to income and poverty to public health.Random sample surveys are staples of political organizations and academics interested in understanding national opinion about important public policies and public o±cials.How do random sample surveys work?A relatively small group of people are chosen at random and interviewed.The average answer to a particular question in a random sample istaken to represent or measure the average answer to that question in the entire population from which the sample is taken.How many people are to be interviewed and what they are to be asked is a matter of choice for the social scientist.But,the power of the random sample survey is that random choice of individuals gives the researcher leverage-allowing for great economy in the study of populations.2.Randomized ExperimentsPeople have conducted controlled experiments for centuries,especially using physical ob-jects.Controls involving creating conditions in which all other factors are held constant. Even with the best controlled experiments,it is possible to leave some potentially important factor uncontrolled.Such uncontrolled factors might create spurious relations or mask im-portant e®ects.Perhaps the most profound contribution of probability theory to scienti¯c study of social and physical behavior is the notion that random assignment of individuals to di®erent experimental conditions(such as receiving a drug or receiving a placebo)can reduce or even eliminate the threat of spurious e®ects.2.Fundamentals of Research MethodsA.Measurement and Estimation1.Concepts and Variables{the constructs or behavior we wish to understand.A good example is\inequality."Exercise:De¯ne inequality.2.Measures{the mathematical representation of the concept.For example,the income distribution in a society might be used to measure inequality.Exercise:devise a measure of the total amount of income inequality in a country.3.Measurement Theory{what requirements do we impose on our measurement device.(i)accuracy(with enough observations we would arrive at the correct answer),(ii) precision(low noise),(iii)reliability(can replicate).B.Model Building1.E®ects and Behavioral RelationshipsSocial scientists freqently want to measure the e®ect of one factor on another.There are many such examples.What is the e®ect of police on crime?What is the e®ect of additional military force on the probability of winning a battle or war?How does class size a®ect educational performance?How do electoral rules,such as single member districts,translate votes into legislative seats?In each case,there is one factor whose levels or values we would like to vary,such as the number of police,in order to observe changes in a second factors,such as the crime rate. The¯rst factor we call an independent variable,and the second factor,a dependent variable.2.AccountingWe seek to make a complete accounting of behavior.In this respect we value models in which a set of variables has high explanatory power.W e also demand parsimony:simpler is better.Example.Housing sales prices can be predicted very well as a function of list prices.In a normal market sales prices are92percent of list prices,and the¯t is extremely good.3.Equilibrium ConceptsMany ideas and conjectures about how social relations produce outcomes:maximizing behavior,dynamic adjustment,e±cient markets,or natural selection.The forces that cre-ate social outcomes make it di±cult to give causal interpretations to observed e®ects or relationships.C.InferenceA fundamental methodological problem is knowing when you should go with one argu-ment or idea or a competing argument or idea.When we measure phenomena,we often thenuse the measurements to draw inferences about di®erent ideas.Are data consistent with an argument or idea?What conclusions can we draw about theories from data?In the end, then,statistics involves a bit of decision theory.Predictions of a theory or conjectures about the world are called hypotheses.When specifying hypotheses it is important to be clear about all of the possible values.In a court of criminal law,hypotheses are questions of guilt or innocence.In medicine,hypotheses are about the condition of the patient,such as whether a cancer is benign or malignant or whether a woman is pregnant or not.In the scienti¯c method generally,the question is whether an conjecture is true or not.Unfortuantely,we never observe the truth.We use data to make decisions about hy-potheses.The evidence brought to a trial are data.A series of test are data.An academic study generates data.The problem of inference is how to use data to make decisions about hypotheses.Ultimately,that will depend on the value we place on di®erent sorts of outcomes from our decisions.However,we can formulate the problem we face quite simply.We want to make the correct decision,and we can make a correct decision one of two ways.First,we may decide,using the data,that the hypothesis is true and the state of the world is such that it is true.Second,we may decide,using the data,that the hypothesis is not true and the state of the world is such that the hypothesis is not true.W e may also make errors two ways.W e may decide that the hypothesis is true when it is in fact false or we may decided that the hypothesis is false when it is infact true.One a central objective of researchers is to avoid either of the two sorts of errors.Sta-tistical design is fundamentally about how to minimize the chances of making a mistaken judgment.。
MIT 教授为博一新生讲授的Stata编程专题英文版---lecture5
• Lots of “if” and “in” commands could slow things down
• Create “1% sample” to develop and test code (to prevent unanticipated crashes after code has been running for hours)
Precision issues in Mata
Large data sets in Stata
• Computer architecture overview
– CPU: executes instructions – RAM (also called the “memory”): stores frequentlyaccessed data – DISK (“hard drive”): stores not-as-frequently used data
Precision issues in Mata
Precision issues in Mata
Mata r = c = 0 A = (1e10, 2e10 \ 2e-10, 3e-10) A rank(A) luinv(A, 1e-15) _equilrc(A, r, c) A r c rank(A) luinv(A) c’:*luinv(A):*r’ end
Overview
• This lecture is part wrap-up lecture, part “tips and tricks” • Focus is on dealing with large data sets and on numerical precision • Numerical precision
国外通信类经典书籍介绍
国外通信类经典书籍1、《Linear Systems and Signals》——thi这本书个人觉得很不错，是一本线性系统和信号的入门好书。
可以适用于通信、电路、控制等专业。
虽说是入门的好书，但是本书的编排是内容由浅入深，讲述可是深入浅出。
我通读全书后，觉得深有体会，看这本书就像在看小说一般，对于一个话题的介绍，往往从其历史发展说起，让你知道其来龙去脉。
不像国内的书，一上来就是定理、定律。
同时，书中每讲完一个知识点，都会有适当的例题让你加深理解。
本书给我的一种感觉就是，作者将一种菜吃透了，消化了，而且掌握了作者这种菜的方法，然后把这种做法告诉你，然你自己去做菜，做出来的菜可能不一样，但是方法你是掌握了。
最根本的你掌握了，做什么菜是你自己的发挥了。
不像国内的教科书，就要你做出一样的菜才是学会了做菜。
这本书讲述了线性系统的一般原理，信号的分析处理，例Fourier变换、Laplace变换、z变换、Hilbert变换等等。
从连续信号说到离散信号，总之是一气呵成，中间似乎看不出什么突变。
对于初学者，这是一本很好的入门书，对于深入者，这又是一本极好的参考书。
极力推荐。
实话说，Lathi的书每看一回都会有新的感觉，常看常新。
2、《Fundamentals of Statistical Signal Processing,Volume I: Estimation Theory》——Steven M. Kay3、《Fundamentals of Statistical Signal Processing,Volume II: Detection Theory》——Steven M. Kay这两本书是Kay的成名作。
我只读过第一卷，因为图书馆只有第一卷:p这两本书比Van Trees的书成书要晚，所以内容比较新。
作者的作风很严谨，书中的推导极其严密。
不失为一位严谨的学者的作风！虽说推导严密，但是本书也不只是单纯讲数学的，与工程应用也很贴近。
introduction to mathematical statistics
Chapter 3
29
3.2.8
The number of missile hits on the plane is binomial with n = 6 and p = 0.2. The probability that the plane will crash is the probability that k ≥ 2. This event is the complement of the event that k = 0 or 1, so the probability is
Probabilities for the second system are binomial with n = 100 and p = 0.02. The probability that k ≥ 1 is 1 − (0.98)100 = 1 − 0.133 = 0.867
System 2 is superior from a bulb replacement perspective.
∑ service =
k
3 =0
⎛10
⎜ ⎝
k
⎞⎟(0.05)k ⎠
(0.95)10−k
= 0.599 + 0.315 + 0.075 + 0.010 = 0.999
3.2.6
Probabilities for the first system are binomial with n = 50 and p = 0.05. The probability that k ≥ 1 is 1 − (0.95)50 = 1 − 0.077 = 0.923.
U5P1_lhospitals-rule-and-improper-integrals
lim
− sin x − cos x = lim . x→0 2x 2
All together, the calculation looks like: lim cos x − 1 x2 = = = = − sin x 2x − cos x lim x→0 2 − cos 0 2 1 − . 2
1
Repeating L’Hˆ opital’s Rule
This example illustrates the superiority of Version 1 of l’Hˆ opital’s rule; it works even if g � (a) = 0. In this case, f (x) = cos x − 1, g (x) = x2 , and a = 0. We’re trying to find: cos x − 1 . x→ 0 x2 lim We can easily verify that f (a) = g (a) = 0. We apply l’Hˆ opital’s rule: lim cos x − 1 − sin x = lim . x→0 x2 2x
Introduction to L’Hˆ opital’s Rule
In this final unit we tie up some loose ends related to calculus and limits. Our first topic is L’Hˆ opital’s rule, which is useful for understanding multiplication and division by infinity. L’Hˆ opital’s rule is also known as L’Hospital’s rule; the circumflex accent indicates that the letter S has been omitted, so the two spellings are equivalent. The two spellings are pronounced identically, with a long O and silent S. L’Hˆ opital’s rule is used to calculate limits of expressions like: x ln x xe−x ln x x as x → 0+ , as x → ∞, as x → ∞.
T.W. ANDERSON (1971). The Statistical Analysis of Time Series. Series in Probability and Ma
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理论物理电子书
理论物理电子书理论物理-电子书0000理论物理基础彭桓武Simons B. Concepts in theoretical physics (Cambridge lecture notes, 2002)(T)(273s)Principles of Modern Physics-N E I L A S H B Y-S T A N L E Y C . M I L L E R-University of ColoradoFUNDAMENTALS OF physics-J. Richard Christman0-mathematical physics李代数李超代数及在物理学中的应用孙洪洲群论.及其在粒子物理学中的应用,.高崇寿.1992群论及其在固体物理中的应用【徐婉棠,喀兴林】群论及其在物理中的应用(马中骐)群论习题精解+(马中骐)群论与量子力学物理系群论讲义物理学中的群论(上册).陶瑞宝物理学中的群论基础 A W 约什Geometry_Topology_and Physics-NakaharaGeometry+and+Physics+(Jürgen Jost)Lee J.M. Differential and physical geometry (draft)(721s)数学物理中的微分几何与拓扑学_汪容.浙大版.1998Differential Geometry, Analysis and Physics 。
Jeffrey M. Lee微分几何学及其在物理学中的应用物理学家用微分几何-侯伯宇-侯伯元物理中的张量孙志铭Arnold vol1，2A Guided Tour Of Mathematical Physics (By Roel Snieder, Department Of Geophysics, Utrecht UniversAbramovitz M., Stegun I.A. (eds.) Handbook of mathematical functions (10ed., NBS, 1972)(T)(1037s)Academic Press, Methods of Modern Mathematical Physics -- Vol. 1, Functional AnCourant, Hilbert - Methods of Mathematical Physics Vol. 1 ENG (578p)Introduction+to+Applied+Mathematics-GilbertStrangIntroduction+to+Mathematical+Physics+(Laurie+Cosse y)Math_method_for_Phy_Ken Riley, Michael Hobson and Stephen Bence Cambridge, 1997Szekeres, Peter - A Course in Modern Mathematical Physics - Groups, Hilbert Spaces and Differenti数学物理方法梁昆淼数学物理方法（R.+柯朗、D.+希尔伯特）数学物理方法吴崇试数学物理学中的微分形式数学物理中的几何方法(B·F·舒茨)特殊函数概论王竹溪物理学中的非线性方程刘式适物理学中的数学方法（李政道）1-Classical Mechanics and Fluid MechanicsClassical Mechanics - Goldstein古典力学(戈德斯坦)Hand, Finch Analytical Mechanics (Cup, 1998)(T)(590S)Structure and Interpretation of Classical Mechanics-Gerald Jay Sussman and Jack Wisdom with Meinhard E. Mayer -MIT Press经典力学张启仁2-Statistical And Thermal Physics理论物理学基础教程丛书统计物理学(苏汝铿)量子统计力学 by 张先蔚量子统计物理学(北京大学物理系)统计物理现代教程（上、下册）(雷克)统计物理中的蒙特卡罗模拟方法（含有热力学，难度适中）Reif. Fundamentals of Statistical And Thermal PhysicsBratteli O , Robinson D W Vol 1 Operator Algebras And Quantum Statistical Mechanics (2Ed , SpringHuang K. Statistical mechanics (2ed., Wiley, 1987)(T)(506s)Reichl L.E. A modern course in statistical physics (2ed, Wiley, 1998)(T)(840s)3-Electrodynamics赵凯华-电磁学上宇宙电动力学_阿尔芬引力论和宇宙论：广义相对论的原理和应用-温伯格相对论物理宇宙学讲义俞允强天体物理学【李宗伟、肖兴华】+时空的大尺度结构（原版）- 霍金简明天文学手册-刘步林广义相对论引论广义相对论dirac广义相对论（刘辽）大众天文学【法】弗拉马利翁Jackson J.D. Classical electrodynamics (3ed., Wiley,1999)(ISBN 047130932X)(600dpi)(K)(T)(833s).d（研究生程度的必读教材）JACKSON经典电动力学（上册）（经典之作）J.A.Wheeler E.F.Taylor Spacetime_PhysicsHerbert Neff - Introductory ElectromagneticsElectromagnetics (Rothwell & Cloud, 2001 CRC Press)Electricity+and+Magnetism-MITcourseCohen-Tannoudji Introduction to quantum electrodynamicsBuch_John Wiley. Sons_An Introduction to Modern Cosmology4-Optics（光学经典，全面、很厚，很难）光学原理上册、下册(m.玻恩 e.沃耳夫)Bass M , Et Al (Eds) Osa Handbook Of Optics, Vol 1 (Mgh, 1995)(1606s)Goodman - Geometrical Optics--p1628 - cambridgeWiley,.Modern.Nonlinear.Optics.Part.I.Advances.in. Chemical.Physics.Volume.119.(2001),.2Ed5-Quantum MechanicsClassical and Quantum ChaosCohen-Tannoudji Quantum Mechanics, Vol 1Galindo A., Pascual P. Quantum mechanics I (Springer,1990)(ISBN 0387514066)(T) (431s)量子系统中的几何相位-A.Bohm等Jack_Simons_-_Quantum MechanicsJohn_Norbury_-_Quantum_Mechanics_for_Undergraduate sMathematics+of+Quantum+Computation-Goong.ChenModern Quantum Mechanics And Solutions For The Exercices (J J Sakurai)Nuclear And Particle Physics-NielsWaletPhillips.-.Introduction.to.quantum.mechanics.(2003 )(T)(284s)Quantum Mechanics - Concepts and Applications-Tarun.BiswasShankar-Principles Of Quantum Mechanics 2nd EditionThe Basic Tools Of Quantum MechanicsThe+Physics+of+Phase+Transitions-P. Papon J. Leblond P.H.E. MeijerLecture Notes in Physics-Time+in+Quantum++Mechanics+1J.G. Muga.R. Sala Mayato?I.L. Egusquiza (Eds.)Zaarur E. Schaum's Outline of Quantum Mechanics.. Including Hundreds of Solved Problems (Schaum,1喀兴林-高等量子力学席夫量子力学-繁体中文版量子力学(Messiah)Vol1量子力学（卷I）.曾谨言量子力学“天龙八部”-张永德量子力学+(苏汝铿)量子力学Fermi量子力学讲义（张永德）量子力学原理(狄拉克)量子论的物理原理量子论与原子结构-吴大遒量子物理学导论(MIT)物理学引论Vol4-A.P.French By Tsungp Lee量子物理-赵凯华高等量子力学-张永德6-Field theory量子场论-温伯格1,2,3An Introduction to Quantum FieldTheory(Peskin,Schroeder)(full and revised)Banks,Modern+Quantum+Field+Theory--A+Concise+Intro ductionField.theory,.Roman.S..(2ed.,.Springer,.2005)Giachetta,Advanced+Classical+Field+Theory经典场论Kleinert H. Quantum field theory and particle physicsItep-PARTICLE-PHYSICS-and-field-theory场论I-M.A.ShifmanQuantum Field Theory R ClarksonQuantum+Field+Theory+(M.Srednicki) Quantum+Field+Theory-David McMahon Sundaresan. Handbook of particle physics (CRC, 2001)(T)(439 Tong-Quantum Field Theory Zinn-Justin. Quantum field theory and critical phenomena (1ed., 1989)(K)(150dpi)(T)(924s) 北大2005量子场论讲义（赵光达）量子场论-清华王青讲义规范场论（胡瑶光）粒子和场【卢里着,董明德等译】量子场论(上)【依捷克森,祖柏尔着,杜东生等译】量子场论A.Zee量子场论F.Mandl-G.Shaw量子场论LEWIS-H.RYDER实时统计场论-徐宏华统计物理学中的量子场论方法-Abrikosov微分几何-统一场论超弦理论导论Elias-Kiritsis张秋光《场论》上册朱洪元+量子场论On Wittens 3-manifold Invariants-Kevin WalkerLectures on Topological Quantum Field Theory-J. M. F. Labastidaa-Carlos LozanobGEOMETRY OF 2D TOPOLOGICAL FIELD THEORIES-Boris DUBROVIN-SISSA, TriesteDunne(1999)-Aspects of Chern-Simons Theorylabastida(1998)-Chern-Simons Gauge Theory-- Ten Years After7-Solid state physics（非常好的书）固体物理学（黄昆）固体物理导论C.KittelMechanics Of Solids-Bela I. Sandor-University of Wisconsin-MadisonKleinert H. Gauge fields in condensed matter physics part1(T)(252s)Ashcroft, Neil W, Mermin, David N - Solid State PhysicsAltland & Simons - Concepts Of Theoretical Solid State Physics。
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MIT教材《Introduction to Statistical Physics》评介研究张立彬（南开大学外国教材中心）杨硕（南开大学物理科学学院）1 出版情况和作者简介《Introduction to Statistical Physics》（《统计物理简介》）是美国麻省理工学院物理系课程编号为8.08的课程“Statistical PhysicsⅡ”使用的教材。
本书于2001年由Taylor & Francis出版社第一次出版，全书共288页，作者是麻省理工学院的Kerson Huang。
Kerson Huang在MIT于1953年获得物理学博士学位，随后他在普林斯顿大学做博士后工作，1957年回到MIT任教直到1999年退休。
退休后他继续从事着量子场论和统计力学方面的研究。
2 创作背景和主要内容这本书是作者在麻省理工学院给高年级的大学本科生上的一学期制的统计物理的讲义，基于作者在MIT的教学经验，它着重介绍经典和量子物理中的热力学。
本书分为这几个部分。
首先，作者以物质现象的理论介绍了热力学，强调了这种方法的功效和美妙。
然后，作者展示了在统计方法的帮助下来推导这些热力学理论的过程，这是用经典力学和量子力学对理想气体的研究做到的。
作者展示了很大范围的物理现象，可以用玻色气体和费米气体的性质解释。
然后作者提出了统计力学的正式方法——正则系综和巨正则系综。
最后一部分作者用微观层次的眼光转到现象学，介绍了用感序参数描述显示在超导性和超流动性里的对称破缺。
作者在涨落上花了很大的时间，从爱因斯坦对布朗运动的描述开始，通过对随机过程和时间系的分析，最后介绍了蒙特卡洛算法。
经过粗略的划分，本书有12章介绍一般话题，有6章讲特殊应用。
课程需要涉及所有的一般话题，而特殊应用每年都有改变。
在一般话题中，1-3章讲热力学，5，6，8章讲理想气体的动力学，9，10章讲玻色气体和费米气体，12，13章讲正式统计力学，17，18章讲随机过程，余下的6章讲的是传递现象、玻色-爱因斯坦方程、布朗运动等。
在每章的最后作者提供了一些习题。
3 本书的特点本书注重的是物理的理解。
本书的内容涵盖了统计物理相关的基本原理，很自然的分成了18章，全书结构清晰、逻辑严谨，主体线索明确，有助于读者从整体上理解这门课程。
作者在写这本书的时候，用讲述的口吻，给读者身临其境的感觉，引导读者进行思考，从而使读者理解书上的内容。
作者在每一章的最后都留有习题，通过习题读者可以很好的理解书上的内容。
本书的另外一个特点是它从讲解基本的知识延伸到了一些相关的前沿的科学问题，如对称性破缺和玻色-爱因斯坦方程，把理论与应用分开来讲解。
这种深入浅出的讲解方式有利于读者的学习，使读者在理解课程要求的知识之外，学以致用，既了解了学科前沿，又提高了学习的兴趣，增强了学习的热情。
4 本书的学术价值统计物理是有关大量物质的行为特性的一门学科，它研究的范围可以从沸腾的水到金属中的超导体。
从根本上说，它是在探索随机过程的规律。
在实践上，这个学科不仅应用在了自然科学和工程上，还应用在了社会科学和经济学上。
《Introduction to Statistical Physics》这本书能够快速有效的引导读者了解物理世界的统计观点，它的目的是引导读者用钻研的眼光学习物理世界里的统计观点，它帮助读者用统计的方法推导出热力学中物质的现象。
并且本书还提供了这门学科在很多物理领域的应用。
5 本书对我国编写统计物理教材的启示首先，从语言上，本书以一种教授的口吻在叙述所学的内容，仿佛一位作者亲自传授课程，带着读者思考、学习，给读者身临其境的感觉。
这样有助于读者的理解，并且能激发读者的学习兴趣。
其次，本书的结构具有逻辑性，把理论知识和实际应用分开来，使读者更容易把握课程的主要内容和整体线索。
最后，本书涵盖了最基本的理论，并且也包含了前沿的学科领域的知识。
章节目录前言xiii1. 宏观观点1 1.1 热力学11.2 热力学变量2 1.3 热力学极限3 1.4 热力学变换41.5 经典理想气体7 1.6 热力学第一定理8 1.7 磁学系统9习题102. 热与熵132.1 热方程132.2 理想气体的应用142.3 卡诺循环162.4 热力学第二定理182.5 绝对温度192.6 温度作为积分因子212.7 熵232.8 理想气体的熵242.9 热力学极限25习题263. 应用热力学303.1 能量方程303.2 可测量系数313.3 熵和损失323.4 温熵图353.5 平衡条件363.6 自由能363.7 吉布斯函数383.8 麦克斯韦关系383.9 化学势39习题404. 相变454.1 一级相变454.2 相平衡条件474.3 克拉贝隆方程484.4 范德瓦尔斯状态方程49 4.5 维里展开514.6 临界点524.7 麦克斯韦解释534.8 温标54习题565. 统计方法605.1 原子观点605.2 相空间625.3 分布函数645.4 各态历经假说655.5 统计系综655.6 正则系综665.7 最概然分布685.8 拉格朗日乘子69习题716. 麦克斯韦-玻尔兹曼分布74 6.1 参数的确定746.2 理想气体压强756.3 能量均分76 6.4 速度分布776.5 熵796.6 热力学的推导806.7 统计波动816.8 玻尔兹曼因子836.9 时间箭头83习题857. 输运现象897.1 无碰撞和流体力学状态89 7.2 麦克斯韦妖917.3 无粘性流体力学917.4 声波937.5 扩散947.6 热传导967.7 粘度977.8 斯托克斯方程98习题998. 量子统计1028.1 热波长1028.2 全同粒子1048.3 状态数1058.4 自旋1078.5 微正则系综1088.6 费米统计1098.7 玻色统计1108.8 参数的确定1118.9 压力1128.10 熵1138.11 自由能1148.12 状态方程1148.13 经典极限115习题1179. 费米气体1199.1 费米能量1199.2 基态1209.3 费米温度1219.4 低温性质1229.5 质点和孔洞1249.6 固体里的电子1259.7 半导体127习题12910. 玻色气体13210.1 光子13210.2 玻色增强13410.3 声子13610.4 德拜比热13710.5 电子比热13910.6 粒子数守恒140习题14111. 玻色-爱因斯坦凝聚14411.1 宏观状态14411.2 凝聚14611.3 状态方程14811.4 比热14911.5 相的形成15011.6 液氮152习题15412. 正则系综15712.1 微正则系综15712.2 经典正则系综15712.3 分布函数16012.4 与热力学的联系16012.5 能量涨落16112.6 自由能的最小化16212.7 经典理想气体16412.8 量子系综16512.9 量子分布函数16712.10 表示法的选择168习题16813. 巨正则系综17313.1 粒子储存17313.2 巨分布函数17313.3 波动系数17413.4 与热力学的联系17513.5 临界涨落17713.6 巨正则系综里的量子气体178 13.7 占有数涨落18013.8 光子涨落18113.9 电子对的生成182习题18414. 有序参数18814.1 平衡破缺18814.2 伊辛旋转模型18914.3 Ginsburg-Landau理论193 14.4 平均场理论19614.5 临界范例19714.6 涨落-耗散理论19914.7 相关长度200 14.8 普适性201习题20215. 超流体20515.1 压缩波方程20515.2 平均场理论20615.3 Gross-Pitaevsky方程208 15.4 量子相的连续性21015.5 超流体淹没21115.6 超导体21315.7 Meissner效应21415.8 量子磁通量21415.9 Josephson连接21615.10 SQUID 220习题22216. 噪声22616.1 热涨落22616.2 Nyquist噪音22716.3 布朗运动22916.4 爱因斯坦理论23116.5 扩散23316.6 爱因斯坦关系23416.7 分子现实23616.8 涨落和耗散237习题23817. 随机过程24017.1 随机和概率24017.2 二项式分布24117.3 泊松分布24317.4 高斯分布24417.5 中心极限理论24517.6 散射噪声247习题24918. 时间系分析25218.1 爱因斯坦路径25218.2 功率谱图和合作关系方程254 18.3 信号和噪声25618.4 跃迁概率25818.5 Markov过程26018.6 Fokker-Planck方程26118.7 Langevin方程26218.8 布朗运动回顾26418.9 蒙特卡洛方法26618.10 伊辛模型的近似268习题270附录：数学引用274 注解281参考文献282索引284【作者简介】张立彬（1964—），男，河北石家庄人，教育部南开大学外国教材中心副教授，现主要从事信息文化、信息技术与物理学外国教材研究；杨硕（1990-），男，河北秦皇岛人，南开大学物理科学学院。
出师表两汉：诸葛亮先帝创业未半而中道崩殂，今天下三分，益州疲弊，此诚危急存亡之秋也。
然侍卫之臣不懈于内，忠志之士忘身于外者，盖追先帝之殊遇，欲报之于陛下也。
诚宜开张圣听，以光先帝遗德，恢弘志士之气，不宜妄自菲薄，引喻失义，以塞忠谏之路也。
宫中府中，俱为一体；陟罚臧否，不宜异同。
若有作奸犯科及为忠善者，宜付有司论其刑赏，以昭陛下平明之理；不宜偏私，使内外异法也。
侍中、侍郎郭攸之、费祎、董允等，此皆良实，志虑忠纯，是以先帝简拔以遗陛下：愚以为宫中之事，事无大小，悉以咨之，然后施行，必能裨补阙漏，有所广益。
将军向宠，性行淑均，晓畅军事，试用于昔日，先帝称之曰“能”，是以众议举宠为督：愚以为营中之事，悉以咨之，必能使行阵和睦，优劣得所。
亲贤臣，远小人，此先汉所以兴隆也；亲小人，远贤臣，此后汉所以倾颓也。
先帝在时，每与臣论此事，未尝不叹息痛恨于桓、灵也。
侍中、尚书、长史、参军，此悉贞良死节之臣，愿陛下亲之、信之，则汉室之隆，可计日而待也。
臣本布衣，躬耕于南阳，苟全性命于乱世，不求闻达于诸侯。
先帝不以臣卑鄙，猥自枉屈，三顾臣于草庐之中，咨臣以当世之事，由是感激，遂许先帝以驱驰。
后值倾覆，受任于败军之际，奉命于危难之间，尔来二十有一年矣。
先帝知臣谨慎，故临崩寄臣以大事也。
受命以来，夙夜忧叹，恐托付不效，以伤先帝之明；故五月渡泸，深入不毛。