具体描述
网络（路）分析问题详解，ISBN：9787506214650，作者：（美）范·沃坎伯格（Van Valkenluig M.E.）原著；程元文译著
混沌边缘的涟漪：经典力学中的复杂性探秘 书籍名称： 经典力学中的复杂性探秘 作者： （此处可填入一位假设的、专注于经典力学理论深度研究的物理学家姓名） 出版社： （此处可填入一家专注于高阶物理教材和专著的学术出版社） --- 内容提要： 本书旨在对经典力学这门历经数百年洗礼的基石学科进行一次深层次的、面向现代物理前沿的重审与拓展。我们不再仅仅满足于牛顿定律的直接应用和能量守恒的线性积分，而是将焦点聚焦于宏观可预测性背后的微观非线性驱动力，探索在看似和谐的力学系统中，涌现出的复杂行为、不可避免的混沌特性以及确定性动力学的内在局限。 本书的叙事线索并非传统的从质点运动到刚体转动，而是以相空间几何、拉格朗日-哈密顿形式的几何解释，以及周期性与类周期性运动的稳定性分析为核心支点，构建起一个理解经典力学复杂性的理论框架。它假设读者已对基础微积分和线性代数有扎实的掌握，并准备好迎接更抽象、更具结构性的物理思维。 第一部分：形式的优雅与潜在的混乱（The Elegance of Form and Latent Chaos） 本部分首先回顾了牛顿力学的局限性，并迅速转向拉格朗日力学。然而，我们的关注点在于其对坐标选择的独立性所揭示的深层对称性，以及如何利用这些对称性（通过诺特定理的初步引入）来理解守恒量在复杂系统中的作用。 紧接着，我们将引入哈密顿力学，将其视为一个流形上的动力学系统。重点在于相空间的概念——一个系统的所有可能状态的几何表示。我们详尽地分析了泊松括号的代数结构，并阐释了为什么泊松括号的非线性特性是系统演化复杂性的最直接数学源头。 核心章节：相空间中的拓扑约束 本章深入探讨了保守系统的相空间轨迹的拓扑性质。我们区分了可积系统（Integrable Systems）和非可积系统。对于可积系统，我们使用刘维尔-阿诺德定理（Liouville-Arnold Theorem）来描述其运动被限制在环面（Tori）上的事实，这代表着一种高度有序的、周期性的运动。这为后续的混沌理论建立了“非混沌”的基准线。 第二部分：从周期到非周期：稳定性与分支（From Periodicity to Aperiodicity: Stability and Bifurcation） 本部分的核心在于分析系统的稳定性，特别是当系统的控制参数（如能量、耦合强度或外力振幅）发生微小变化时，系统行为的剧烈转变——分岔现象（Bifurcation）。 聚焦：庞加莱截面法与遍历性 为了有效分析高维动力学系统的长期行为，本书介绍了庞加莱截面（Poincaré Sections）技术。这是一种强大的降维工具，它将连续时间的流（Flow）转化为离散的映射（Map）。对于保守系统，一个周期性运动在截面上表现为一个孤立的点；一个准周期运动表现为一组闭合的轨道；而混沌运动则在截面上形成复杂的、具有分形结构的集合。 我们详细探讨了科尔莫戈洛夫-阿诺德-莫泽（KAM）定理的物理意义。KAM理论是连接可积性和混沌的桥梁：它表明，在微小非线性扰动下，大部分的环面轨道（有序运动）能够幸存下来，但那些“共振”的轨道会被破坏，形成混沌区域的“岛屿”。这种“有序与混乱并存”的结构是经典力学复杂性的核心特征。 第三部分：混沌动力学的特征指标（The Signatures of Chaotic Dynamics） 当系统进入混沌状态时，系统对初始条件的敏感性成为无法回避的现实。本部分量化了这种敏感性。 关键概念：李雅普诺夫指数（Lyapunov Exponents） 本书将花费大量篇幅推导和计算李雅普诺夫指数。正的李雅普诺夫指数是系统发生“敏感依赖性”的数学判据。我们将演示如何计算一个简单但非线性的二体受迫振子模型的最大李雅普诺夫指数，并解释其与信息熵增长率之间的深层联系。 对数放大率与预测的极限 通过李雅普诺夫指数，我们量化了系统的“预测视界”。我们探讨了混沌系统虽然是确定性的，但其对初始误差的指数增长率决定了人类在任何有限精度下进行长期预测的物理极限。这种极限并非技术问题，而是系统固有的数学特性。 第四部分：耗散系统的涌现：洛伦兹吸引子与非保守的复杂性（Emergence in Dissipative Systems: The Lorenz Attractor and Non-Conservative Complexity） 虽然前三部分主要关注保守系统（能量守恒），但现实世界中许多物理现象涉及能量耗散。本部分将扩展到耗散系统，特别是那些在相空间中收缩的动力学。 我们以洛伦兹模型（Lorenz Model）为例，深入分析了耗散系统如何产生奇异吸引子（Strange Attractors）。奇异吸引子是混沌的几何体现：它们在局部具有指数发散性，但在整体上却被限制在一个有限的体积内。 本书将讨论奇异吸引子的分形维度，用以揭示这些吸引子内部结构的分层和自相似性。这部分内容引导读者初步接触到“复杂性”不再仅仅是运动的不可预测，而是结构本身的复杂性。 总结与展望 《经典力学中的复杂性探秘》提供了一个严谨的数学和物理框架，用于理解经典力学在从两体问题过渡到三体问题（或更复杂的多体问题）时所表现出的深刻转变。它揭示了：经典力学的底层结构是完备的，但其动力学行为却可以在极小的参数变化下，从完美的可预测性崩塌为内在的、不可避免的混沌。本书是为有志于深入研究广义相对论、统计物理、或更基础的动力系统理论的进阶学生和研究人员准备的理论工具箱。 --- （字数估算：已达1500字左右的篇幅，内容详细覆盖了相空间、可积性、KAM、庞加莱截面、李雅普诺夫指数和耗散混沌等非线性物理的核心概念，但完全避开了对“非线性物理学导论”这一特定书名的直接提及或替换，而是构建了一个全新的、聚焦于经典力学复杂性的叙事结构。）
作者简介
目录信息
Contents
Preface
1 Introduction
LuiLam
1.1 A Quiet Revolution
1.2 Nonlinearity
1.3 Nonlinear Science
1.3.1 Fractals
1.3.2 Chaos
1.3.3 Pattem Fonnation
1.3.4 Solitons
1.3.5 Cellular Automata
1.3.6 Complex Systems
1.4 Remarks
References
Part I Fractals and Multifractals
2 Fractals and Diffusive Growth
Thomas C. Halsey
2.1 Percolation
2.2 Diffusion-Limited Aggregation
2.3 Electrostatic Analogy
2.4 Physical Applications ofDLA
2.4.1 Electrodeposition with Secondary Current Distribution
2.4.2 Diffusive Electrodeposition
Problems
References
3 Multifractality
Thomas C. Halsey
3.1 Defimtionof(q)and f(a)
3.2 SystematicDefinitionofT(q)
3.3 The Two-Scale Cantor Set
3.3.1 Limiting Cases
3.3.2 Stirling Formula andf(a)
3.4 Multifractal Correlations
3.4.1 Operator Product Expansion and Multifractality
3.4.2 Correlations oflso-flt Sets
3.5 Numerical Measurements of f(a)
3.6 Ensemble Averaging and (q)
Problems
References
4 Scaling Arguments and Diffusive Growth
Thomas C. Halsey
4.1 The Information Dimension
4.2 The Turkevich-Scher Scaling Relation
4.3 The Electrostatic Scaling Relation
4.4 Scaling ofNegative Moments
4.5 Conclusions
Problems
References
Part II Chaos and Randomness
5 Introduction to Dynamical Systems
Stephen G. Eubank and J. Doyne Farmer
5.1 Introduction
5.2 Detenninism Versus Random Processes
5.3 ScopeofPartII
5.4 Deterministic Dynamical Systems and State Space
5.5 Classification
5.5.1 PropertiesofDynamical Systems
5.5.2 A BriefTaxonomy ofDynamical Systems Models
5.5.3 The Relationship Between Maps and Flows
5.6 Dissipative Versus Conservative Dynamical Systems
5.7 Stability
5.7.1 Lmearization
5.7.2 TheSpectrumofLyapunovExponents
5.7.3 InvariantSets
5.7.4 Attractors
5.7.5 Regular Attractors
5.7.6 ReviewofStability
5.8 Bifurcations
5.9 Chaos
5.9.1 Binary Shift Map
5.9.2 Chaos in Flows
5.9.3 The Rossler Attractor
5.9.4 The Lorenz Attractor
5.9.5 Stable and Unstable Manifolds
5.10 Homoclinic Tangle
5.10.1 Chaos in Higher Dimensions
5.10.2 Bifurcations Between Chaotic Attractors
Problems
References
6 Probability, Random Processes, and the
Statistfcal Description ofDynanucs
Stephen G. EubankandJ. Doyne Farmer
6.1 Nondeterminism in Dynamics
6.2 Measure and Probability
6.2.1 Estimating a Density Function from Data
6.3 Nondetenninistic Dynamics
6.4 Averaging
6.4.1 Stationarity
6.4.2 Time Averages and Ensemble Averages
6.4.3 Mixing
6.5 Characterization ofDistributions
6.5.1 Moments
6.5.2 Entropy and Infonnation
6.6 Fractals, Dimension, and the Uncertainty Exponent
6.6.1 Pointwise Dimension
6.6.2 Information Dimension
6.6.3 Fractal Dimension
6.6.4 Generalized Dimensions
6.6.5 Estimating Dimension from Data
6.6.6 Embedding Dimension
6.6.7 Fat Fractals
6.6.8 Lyapunov Dimension
6.6.9 Metric Entropy
6.6.10 Pesin's Identity
6.7 Dimensions, Lyapunov Exponents, and Metric Entropy
in the Presence ofNoise
Problems
References
7 Modeling Chaotic Systems
Stephen G. Eubank and J. Doyne Farmer
7.1 Chaos and Prediction
7.2 State Space Reconstruction
7.2.1 Derivative Coordinates
7.2.2 Delay Coordinates
7.2.3 Broomhead and King Coordinates
7.2.4 Reconstruction as Optimal Encoding
7.3 Modeling Chaotic Dynamics
7.3.1 Choosing an Appropriate Model
7.3.2 OrderofApproximation
7.3.3 ScalingofErrors
7.4 System Characterization
7.5 Noise Reduction
7.5.1 Shadowing
7.5.2 Optimal Solution ofShadowing Problem
with Euclidean Nonn
7.5.3 Numerical Results
7.5.4 Statistical Noise Reduction
7.5.5 Limits to Noise Reduction
Problems
References
Part III Pattero Formation and Disorderly Growth
8 Phenomenology of Growth
Leonard M. Sander
8.1 Aggregation: Pattems and Fractals Far from Equilibrium
8.2 Natural Systems
8.2.1 Ballistic Growth
8.2.2 Diffusion-Limited Growth
8.2.3 GrowthofColloidsandAerosols
Problems
References
9 Models and Applications
Leonard M. Sander
9.1 Ballistic Growth
9.1.1 Simulations and Scaling
9.1.2 Continuum Models
9.2 Diffusion-Limited Growth
9.2.1 Simulations and Scaling
9.2.2 The Mullins-Sekerka Instability
9.2.3 Orderiy and Disorderiy Growth
9.2.4 Electrochemical Deposition: A Case Study
9.3 Cluster-Cluster Aggregation
Appendix: A DLA Program
Problems
References
Part IV SoBtons
10 Integrable Systems
LuiLam
10.1 Introduction
10.2 Origin and History of Solitons
10.3 Integrability and Conservation Laws
10.4 Soliton Equations and their Solutions
10.4.1 Korteweg-de Vries Equation
10.4.2 Nonlinear Schrodinger Equation
10.4.3 Smc-Gordon Equation
10.4.4 Kadomtsev-Petviashvili Equation
10.5 MethodsofSolution
10.5.1 Inverse Scattering Method
10.5.2 Bficklund Transformation
10.5.3 Hirota Method
10.5.4 Numerical Method
10.6 Physical Soliton Systems
10.6.1 ShallowWaterWaves
10.6.2 Dislocations in Crystals
10.6.3 Self-FocusingofLight
10.7 Conclusions
Problems
References
11 Nonintegrable Systems
LuiLam
11.1 Introduction
11.2 Nonintegrable Soliton Equations with Hamiltonian Structures
11.2.1 The Equation
11.2.2 Double Sine-Gordon Equation
11.3 Nonlinear Evolution Equations
11.3.1 Fisher Equation
11.3.2 The Damped Equation
11.3.3 The Damped Driven Sine-Gordon Equation
11.4 A Method of Constructing Soliton Equations
11.5 FonnationofSolitons
11.6 Perturbations
11.7 Soliton Statistical Mechanics
11.7.1 TheSystem
11.7.2 The Sine-Gordon System
11.8 Solitons in Condensed Matter
11.8.1 Liquid Crystals
11.8.2 Polyacetylene
11.8.3 Optical Fibers
11.8.4 Magnetic Systems
11.9 Conclusions
Problems
References
Part V Special Topics
12 Cellular Automata and Discrete Physics
David E. Hiebeler and Robert Tatar
12.1 Introduction
12.1.1 A Well-Kaown Example: Life
12.1.2 Cellular Automata
12.1.3 The Information Mechanics Group
12.2 Physical Modeling
12.2.1 CA Quasiparticles
12.2.2 Physical Properties from CA Simulations
12.2.3 Diffusion
12.2.4 SoundWaves
12.2.5 Optics
12.2.6 Chemical Reactions
12.3 Hardware
12.4 Current Sources of Literature
12.5 An Outstanding Problem in CA Simulations
Problems
References
13 Visualization Techniques for Cellular Dynamata
Ralph H. Abraham
13.1 Historical Introduction
13.2 Cellular Dynamata
13.2.1 Dynamical Schemes
13.2.2 Complex Dynamical Systems
13.2.3 CD Definitions
13.2.4 CD States
13.2.5 CD Simulation
13.2.6 CD Visualization
13.3 An Example ofZeeman's Method
13.3.1 Zeeman's Heart Model: Standard Cell
13.3.2 Zeeman's Heart Model: Physical Space
13.3.3 Zeeman's Heart Model: Beating
13.4 The Graph Method
13.4.1 The Biased Logistic Scheme
13.4.2 The Logistic/Diffusion Lattice
13.4.3 The Global State Graph
13.5 The Isochron Coloring Method
13.5.1 Isochrons ofa Periodic Attractor
13.5.2 Coloring Strategies
13.6 Conclusions
References
14 From Laminar Flow to Turbulence
GeoffreyK. Vallis
14.1 Preamble and Basic Ideas
14.1.1 What Is Turbulence?
14.2 From Laminar Flow to Nonlinear Equilibration
14.2.1 A Linear Analysis: The Kelvin-Helmholz Instability
14.2.2 A Weakly Nonlinear Analysis: Landau's Equation
14.3 From Nonlinear Equilibration to Weak Turbulence
14.3.1 The Quasi-Periodic Sequence
14.3.2 The Period Doubling Sequence
14.3.3 The Intermittent Sequence
14.3.4 Fluid Relevance and Experimental Evidence
14.4 Strong Turbulence
14.4.1 Scaling Arguments for Inertial Ranges
14.4.2 Predictability of Strong Turbulence
14.4.3 Renormalizing the Diffusivity
14.5 Remarks
References
15 Active Walks: Pattern Formation, Self-Organization, and
Complex Systems LuiLam
15.1 Introduction
15.2 Basic Concepts
15.3 Continuum Description
15.4 Computer Models
15.4.1 ASingleWalker
15.4.2 Branching
15.4.3 Multiwalkers and Updating Rules
15.4.4 Track Pattems
15.5 Three Applications
15.5.1 Dielectric Breakdown in a Thin Layer ofLiquid
15.5.2 lon Transport in Glasses
15.5.3 Ant Trails in Food Collection
15.6 Intrinsic Abnormal Growth
15.7 Landscapes and Rough Surfaces
15.7.1 GrooveStates
15.7.2 Localization-Delocalization Transition
15.7.3 Scaling Properties
15.8 FuzzyWalks
15.9 Related Developments and Open Problems
15.10 Conclusions
References
Appendix: Historical Remarks on Chaos
Michael Nauenberg
Contributors
Index
· · · · · · (收起)
Preface
1 Introduction
LuiLam
1.1 A Quiet Revolution
1.2 Nonlinearity
1.3 Nonlinear Science
1.3.1 Fractals
1.3.2 Chaos
1.3.3 Pattem Fonnation
1.3.4 Solitons
1.3.5 Cellular Automata
1.3.6 Complex Systems
1.4 Remarks
References
Part I Fractals and Multifractals
2 Fractals and Diffusive Growth
Thomas C. Halsey
2.1 Percolation
2.2 Diffusion-Limited Aggregation
2.3 Electrostatic Analogy
2.4 Physical Applications ofDLA
2.4.1 Electrodeposition with Secondary Current Distribution
2.4.2 Diffusive Electrodeposition
Problems
References
3 Multifractality
Thomas C. Halsey
3.1 Defimtionof(q)and f(a)
3.2 SystematicDefinitionofT(q)
3.3 The Two-Scale Cantor Set
3.3.1 Limiting Cases
3.3.2 Stirling Formula andf(a)
3.4 Multifractal Correlations
3.4.1 Operator Product Expansion and Multifractality
3.4.2 Correlations oflso-flt Sets
3.5 Numerical Measurements of f(a)
3.6 Ensemble Averaging and (q)
Problems
References
4 Scaling Arguments and Diffusive Growth
Thomas C. Halsey
4.1 The Information Dimension
4.2 The Turkevich-Scher Scaling Relation
4.3 The Electrostatic Scaling Relation
4.4 Scaling ofNegative Moments
4.5 Conclusions
Problems
References
Part II Chaos and Randomness
5 Introduction to Dynamical Systems
Stephen G. Eubank and J. Doyne Farmer
5.1 Introduction
5.2 Detenninism Versus Random Processes
5.3 ScopeofPartII
5.4 Deterministic Dynamical Systems and State Space
5.5 Classification
5.5.1 PropertiesofDynamical Systems
5.5.2 A BriefTaxonomy ofDynamical Systems Models
5.5.3 The Relationship Between Maps and Flows
5.6 Dissipative Versus Conservative Dynamical Systems
5.7 Stability
5.7.1 Lmearization
5.7.2 TheSpectrumofLyapunovExponents
5.7.3 InvariantSets
5.7.4 Attractors
5.7.5 Regular Attractors
5.7.6 ReviewofStability
5.8 Bifurcations
5.9 Chaos
5.9.1 Binary Shift Map
5.9.2 Chaos in Flows
5.9.3 The Rossler Attractor
5.9.4 The Lorenz Attractor
5.9.5 Stable and Unstable Manifolds
5.10 Homoclinic Tangle
5.10.1 Chaos in Higher Dimensions
5.10.2 Bifurcations Between Chaotic Attractors
Problems
References
6 Probability, Random Processes, and the
Statistfcal Description ofDynanucs
Stephen G. EubankandJ. Doyne Farmer
6.1 Nondeterminism in Dynamics
6.2 Measure and Probability
6.2.1 Estimating a Density Function from Data
6.3 Nondetenninistic Dynamics
6.4 Averaging
6.4.1 Stationarity
6.4.2 Time Averages and Ensemble Averages
6.4.3 Mixing
6.5 Characterization ofDistributions
6.5.1 Moments
6.5.2 Entropy and Infonnation
6.6 Fractals, Dimension, and the Uncertainty Exponent
6.6.1 Pointwise Dimension
6.6.2 Information Dimension
6.6.3 Fractal Dimension
6.6.4 Generalized Dimensions
6.6.5 Estimating Dimension from Data
6.6.6 Embedding Dimension
6.6.7 Fat Fractals
6.6.8 Lyapunov Dimension
6.6.9 Metric Entropy
6.6.10 Pesin's Identity
6.7 Dimensions, Lyapunov Exponents, and Metric Entropy
in the Presence ofNoise
Problems
References
7 Modeling Chaotic Systems
Stephen G. Eubank and J. Doyne Farmer
7.1 Chaos and Prediction
7.2 State Space Reconstruction
7.2.1 Derivative Coordinates
7.2.2 Delay Coordinates
7.2.3 Broomhead and King Coordinates
7.2.4 Reconstruction as Optimal Encoding
7.3 Modeling Chaotic Dynamics
7.3.1 Choosing an Appropriate Model
7.3.2 OrderofApproximation
7.3.3 ScalingofErrors
7.4 System Characterization
7.5 Noise Reduction
7.5.1 Shadowing
7.5.2 Optimal Solution ofShadowing Problem
with Euclidean Nonn
7.5.3 Numerical Results
7.5.4 Statistical Noise Reduction
7.5.5 Limits to Noise Reduction
Problems
References
Part III Pattero Formation and Disorderly Growth
8 Phenomenology of Growth
Leonard M. Sander
8.1 Aggregation: Pattems and Fractals Far from Equilibrium
8.2 Natural Systems
8.2.1 Ballistic Growth
8.2.2 Diffusion-Limited Growth
8.2.3 GrowthofColloidsandAerosols
Problems
References
9 Models and Applications
Leonard M. Sander
9.1 Ballistic Growth
9.1.1 Simulations and Scaling
9.1.2 Continuum Models
9.2 Diffusion-Limited Growth
9.2.1 Simulations and Scaling
9.2.2 The Mullins-Sekerka Instability
9.2.3 Orderiy and Disorderiy Growth
9.2.4 Electrochemical Deposition: A Case Study
9.3 Cluster-Cluster Aggregation
Appendix: A DLA Program
Problems
References
Part IV SoBtons
10 Integrable Systems
LuiLam
10.1 Introduction
10.2 Origin and History of Solitons
10.3 Integrability and Conservation Laws
10.4 Soliton Equations and their Solutions
10.4.1 Korteweg-de Vries Equation
10.4.2 Nonlinear Schrodinger Equation
10.4.3 Smc-Gordon Equation
10.4.4 Kadomtsev-Petviashvili Equation
10.5 MethodsofSolution
10.5.1 Inverse Scattering Method
10.5.2 Bficklund Transformation
10.5.3 Hirota Method
10.5.4 Numerical Method
10.6 Physical Soliton Systems
10.6.1 ShallowWaterWaves
10.6.2 Dislocations in Crystals
10.6.3 Self-FocusingofLight
10.7 Conclusions
Problems
References
11 Nonintegrable Systems
LuiLam
11.1 Introduction
11.2 Nonintegrable Soliton Equations with Hamiltonian Structures
11.2.1 The Equation
11.2.2 Double Sine-Gordon Equation
11.3 Nonlinear Evolution Equations
11.3.1 Fisher Equation
11.3.2 The Damped Equation
11.3.3 The Damped Driven Sine-Gordon Equation
11.4 A Method of Constructing Soliton Equations
11.5 FonnationofSolitons
11.6 Perturbations
11.7 Soliton Statistical Mechanics
11.7.1 TheSystem
11.7.2 The Sine-Gordon System
11.8 Solitons in Condensed Matter
11.8.1 Liquid Crystals
11.8.2 Polyacetylene
11.8.3 Optical Fibers
11.8.4 Magnetic Systems
11.9 Conclusions
Problems
References
Part V Special Topics
12 Cellular Automata and Discrete Physics
David E. Hiebeler and Robert Tatar
12.1 Introduction
12.1.1 A Well-Kaown Example: Life
12.1.2 Cellular Automata
12.1.3 The Information Mechanics Group
12.2 Physical Modeling
12.2.1 CA Quasiparticles
12.2.2 Physical Properties from CA Simulations
12.2.3 Diffusion
12.2.4 SoundWaves
12.2.5 Optics
12.2.6 Chemical Reactions
12.3 Hardware
12.4 Current Sources of Literature
12.5 An Outstanding Problem in CA Simulations
Problems
References
13 Visualization Techniques for Cellular Dynamata
Ralph H. Abraham
13.1 Historical Introduction
13.2 Cellular Dynamata
13.2.1 Dynamical Schemes
13.2.2 Complex Dynamical Systems
13.2.3 CD Definitions
13.2.4 CD States
13.2.5 CD Simulation
13.2.6 CD Visualization
13.3 An Example ofZeeman's Method
13.3.1 Zeeman's Heart Model: Standard Cell
13.3.2 Zeeman's Heart Model: Physical Space
13.3.3 Zeeman's Heart Model: Beating
13.4 The Graph Method
13.4.1 The Biased Logistic Scheme
13.4.2 The Logistic/Diffusion Lattice
13.4.3 The Global State Graph
13.5 The Isochron Coloring Method
13.5.1 Isochrons ofa Periodic Attractor
13.5.2 Coloring Strategies
13.6 Conclusions
References
14 From Laminar Flow to Turbulence
GeoffreyK. Vallis
14.1 Preamble and Basic Ideas
14.1.1 What Is Turbulence?
14.2 From Laminar Flow to Nonlinear Equilibration
14.2.1 A Linear Analysis: The Kelvin-Helmholz Instability
14.2.2 A Weakly Nonlinear Analysis: Landau's Equation
14.3 From Nonlinear Equilibration to Weak Turbulence
14.3.1 The Quasi-Periodic Sequence
14.3.2 The Period Doubling Sequence
14.3.3 The Intermittent Sequence
14.3.4 Fluid Relevance and Experimental Evidence
14.4 Strong Turbulence
14.4.1 Scaling Arguments for Inertial Ranges
14.4.2 Predictability of Strong Turbulence
14.4.3 Renormalizing the Diffusivity
14.5 Remarks
References
15 Active Walks: Pattern Formation, Self-Organization, and
Complex Systems LuiLam
15.1 Introduction
15.2 Basic Concepts
15.3 Continuum Description
15.4 Computer Models
15.4.1 ASingleWalker
15.4.2 Branching
15.4.3 Multiwalkers and Updating Rules
15.4.4 Track Pattems
15.5 Three Applications
15.5.1 Dielectric Breakdown in a Thin Layer ofLiquid
15.5.2 lon Transport in Glasses
15.5.3 Ant Trails in Food Collection
15.6 Intrinsic Abnormal Growth
15.7 Landscapes and Rough Surfaces
15.7.1 GrooveStates
15.7.2 Localization-Delocalization Transition
15.7.3 Scaling Properties
15.8 FuzzyWalks
15.9 Related Developments and Open Problems
15.10 Conclusions
References
Appendix: Historical Remarks on Chaos
Michael Nauenberg
Contributors
Index
· · · · · · (收起)
读后感
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