《統計物理學中的蒙特卡羅模擬(第5版)(英文版)》主要處理凝聚態物理學的多體系統和相關物理學、化學及其他方面的計算模擬，甚至滲透到交通流、股票市場波動等等領域。書中描述了多變數蒙特卡洛模擬方法的理論背景，給出了初學者學習進行模擬和結果分析的系統演示。
基本介紹
- 書名：統計物理學中的蒙特卡羅模擬
- 作者：賓德 (Kurt Binder)
- 出版日期：2014年3月1日
- 語種：簡體中文, 英語
- ISBN：9787510070761
- 外文名：Monte Carlo Simulation in Statistical Physics:An Introduction
- 出版社：世界圖書出版公司北京公司
- 頁數：200頁
- 開本：24
- 品牌：世界圖書出版公司北京公司
基本介紹,內容簡介,作者簡介,圖書目錄,
基本介紹
內容簡介
《統計物理學中的蒙特卡羅模擬(第5版)(英文版)》不僅包括經典方法，也包括蒙特卡洛模擬方法；增加了一章專門講述自由能景觀採樣。
作者簡介
作者：（德國）賓德（Kurt Binder） （德國）Dieter W.Heermann
圖書目錄
1 Introduction: Purpose And Scope of This Volume, And Some General Comments
2 Theoretical Foundations of The Monte Carlo Method And Its Applications In Statistical Physics
2.1 Simple Sampling Versus Importance Sampling
2.L.1 Models
2.1.2 Simple Sampling
2.1.3 Random Walks and Self-Avoiding Walks
2.1.4 Thermal Averages By the Simple Sampling Method
2.1.5 Advantages and Limitations of Simple Sampling
2.1.6 Importance Sampling
2.1.7 More About Models And Algorithms
2.2 Organization of Monte Carlo Programs, and the Dynamic Interpretation of Monte Carlo Sampling
2.2.1 First Comments on The Simulation of The Ising Model
2.2.2 Boundary Conditions
2.2.3 The Dynamic Interpretation of The Importance Sampling Monte Carlo Method
2.2.4 Statistical Errors and Time-Displaced Relaxation Functions
2.3 Finite-Size Effects
2.3.1 Finite-Size Effects At The Percolation Transition
2.3.2 Finite-Size Scaling For The Percolation Problem
2.3.3 Broken Symmetry And Finite-Size Effects At Thermal Phase Transitions
2.3.4 The Order Parameter Probability Distribution And Its Use to Justify Finite-Size Scaling And Phenomenological Renormalization
2.3.5 Finite-Size Behavior of Relaxation Times
2.3.6 Finite-Size Scaling Without "Hyperscaling"
2.3.7 Finite-Size Scaling For First-Order Phase Transitions
2.3.8 Finite-Size Behavior of Statistical Errors And the Problem Of Self-Averaging
2.4 Remarks on The Scope of The Theory Chapter
3 Guide to Practical Work With The Monte Carlo Method
3.1 Aims of The Guide
3.2 Simple Sampling
3.2.1 Random Walk
3.2.2 Nonreversal Random Walk
3.2.3 Self-Avoiding Random Walk
3.2.4 Percolation
3.3 Biased Sampling
3.3.1 Self-Avoiding Random Walk
3.4 Importance Sampling
3.4.1 Ising Model
3.4.2 Self-Avoiding Random Walk
4 Some Important Recent Developments Of The Monte Carlo Methodology
4.1 Introduction
4.2 Application of the Swendsen-Wang Cluster Algorithm To The Ising Model
4.3 Reweighting Methods In The Study Of Phase Diagrams,First-Order Phase Transitions, And Interfacial Tensions
4.4 Some Comments On Advances With Finite-Size Scaling Analyses
5 Quantum Monte Carlo Simulations: An Introduction
5.1 Quantum Statistical Mechanics Versus Classical Statistical Mechanics
5.2 The Path Integral Quantum Monte Carlo Method
5.3 Quantum Monte Carlo For Lattice Models
5.4 Concluding Remarks
6 Monte Carlo Methods For The Sampling of Free Energy Landscapes.
6.1 Introduction And Overview
6.2 Umbrella Sampling
6.3 Multicanonical Sampling And Other "Extended Ensemble" Methods
6.4 Wang-Landau Sampling
6.5 Transition Path Sampling
6.6 Concluding Remarks
Appendix
A.1 Algorithm For The Random Walk Problem
A.2 Algorithm For Cluster Identification
References
Bibliography
Subject Index
2 Theoretical Foundations of The Monte Carlo Method And Its Applications In Statistical Physics
2.1 Simple Sampling Versus Importance Sampling
2.L.1 Models
2.1.2 Simple Sampling
2.1.3 Random Walks and Self-Avoiding Walks
2.1.4 Thermal Averages By the Simple Sampling Method
2.1.5 Advantages and Limitations of Simple Sampling
2.1.6 Importance Sampling
2.1.7 More About Models And Algorithms
2.2 Organization of Monte Carlo Programs, and the Dynamic Interpretation of Monte Carlo Sampling
2.2.1 First Comments on The Simulation of The Ising Model
2.2.2 Boundary Conditions
2.2.3 The Dynamic Interpretation of The Importance Sampling Monte Carlo Method
2.2.4 Statistical Errors and Time-Displaced Relaxation Functions
2.3 Finite-Size Effects
2.3.1 Finite-Size Effects At The Percolation Transition
2.3.2 Finite-Size Scaling For The Percolation Problem
2.3.3 Broken Symmetry And Finite-Size Effects At Thermal Phase Transitions
2.3.4 The Order Parameter Probability Distribution And Its Use to Justify Finite-Size Scaling And Phenomenological Renormalization
2.3.5 Finite-Size Behavior of Relaxation Times
2.3.6 Finite-Size Scaling Without "Hyperscaling"
2.3.7 Finite-Size Scaling For First-Order Phase Transitions
2.3.8 Finite-Size Behavior of Statistical Errors And the Problem Of Self-Averaging
2.4 Remarks on The Scope of The Theory Chapter
3 Guide to Practical Work With The Monte Carlo Method
3.1 Aims of The Guide
3.2 Simple Sampling
3.2.1 Random Walk
3.2.2 Nonreversal Random Walk
3.2.3 Self-Avoiding Random Walk
3.2.4 Percolation
3.3 Biased Sampling
3.3.1 Self-Avoiding Random Walk
3.4 Importance Sampling
3.4.1 Ising Model
3.4.2 Self-Avoiding Random Walk
4 Some Important Recent Developments Of The Monte Carlo Methodology
4.1 Introduction
4.2 Application of the Swendsen-Wang Cluster Algorithm To The Ising Model
4.3 Reweighting Methods In The Study Of Phase Diagrams,First-Order Phase Transitions, And Interfacial Tensions
4.4 Some Comments On Advances With Finite-Size Scaling Analyses
5 Quantum Monte Carlo Simulations: An Introduction
5.1 Quantum Statistical Mechanics Versus Classical Statistical Mechanics
5.2 The Path Integral Quantum Monte Carlo Method
5.3 Quantum Monte Carlo For Lattice Models
5.4 Concluding Remarks
6 Monte Carlo Methods For The Sampling of Free Energy Landscapes.
6.1 Introduction And Overview
6.2 Umbrella Sampling
6.3 Multicanonical Sampling And Other "Extended Ensemble" Methods
6.4 Wang-Landau Sampling
6.5 Transition Path Sampling
6.6 Concluding Remarks
Appendix
A.1 Algorithm For The Random Walk Problem
A.2 Algorithm For Cluster Identification
References
Bibliography
Subject Index
