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E-raamat: Path Integrals for Stochastic Processes: An Introduction [World Scientific e-raamat]

(Univ De Cantabria & Csic, Spain)
  • Formaat: 176 pages
  • Ilmumisaeg: 18-Mar-2013
  • Kirjastus: World Scientific Publishing Co Pte Ltd
  • ISBN-13: 9789814449045
Teised raamatud teemal:
  • World Scientific e-raamat
  • Hind: 78,54 €*
  • * hind, mis tagab piiramatu üheaegsete kasutajate arvuga ligipääsu piiramatuks ajaks
  • Formaat: 176 pages
  • Ilmumisaeg: 18-Mar-2013
  • Kirjastus: World Scientific Publishing Co Pte Ltd
  • ISBN-13: 9789814449045
Teised raamatud teemal:
This book provides an introductory albeit solid presentation of path integration techniques as applied to the field of stochastic processes. The subject began with the work of Wiener during the 1920's, corresponding to a sum over random trajectories, anticipating by two decades Feynman's famous work on the path integral representation of quantum mechanics. However, the true trigger for the application of these techniques within nonequilibrium statistical mechanics and stochastic processes was the work of Onsager and Machlup in the early 1950's. The last quarter of the 20th century has witnessed a growing interest in this technique and its application in several branches of research, even outside physics (for instance, in economy).The aim of this book is to offer a brief but complete presentation of the path integral approach to stochastic processes. It could be used as an advanced textbook for graduate students and even ambitious undergraduates in physics. It describes how to apply these techniques for both Markov and non-Markov processes. The path expansion (or semiclassical approximation) is discussed and adapted to the stochastic context. Also, some examples of nonlinear transformations and some applications are discussed, as well as examples of rather unusual applications. An extensive bibliography is included. The book is detailed enough to capture the interest of the curious reader, and complete enough to provide a solid background to explore the research literature and start exploiting the learned material in real situations. remove
Preface vii
1 Stochastic Processes: A Short Tour 1(12)
1.1 Stochastic Process
1(3)
1.2 Master Equation
4(1)
1.3 Langevin Equation
5(2)
1.4 Fokker-Planck Equation
7(2)
1.5 Relation Between Langevin and Fokker-Planck Equations
9(4)
2 The Path Integral for a Markov Stochastic Process 13(14)
2.1 The Wiener Integral
13(3)
2.2 The Path Integral for a General Markov Process
16(4)
2.3 The Recovering of the Fokker-Planck Equation
20(1)
2.4 Path Integrals in Phase Space
21(3)
2.5 Generating Functional and Correlations
24(3)
3 Generalized Path Expansion Scheme I 27(6)
3.1 Expansion Around the Reference Path
27(3)
3.2 Fluctuations Around the Reference Path
30(3)
4 Space-Time Transformation I 33(14)
4.1 Introduction
33(1)
4.2 Simple Example
34(5)
4.3 Fluctuation Theorems from Non-equilibrium Onsager-Machlup Theory
39(2)
4.4 Brownian Particle in a Time-Dependent Harmonic Potential
41(2)
4.5 Work Distribution Function
43(4)
5 Generalized Path Expansion Scheme II 47(8)
5.1 Path Expansion: Further Aspects
47(4)
5.2 Examples
51(4)
5.2.1 Ornstein-Uhlenbeck Problem
51(1)
5.2.2 Simplified Prey-Predator Model
52(3)
6 Space-Time Transformation II 55(16)
6.1 Introduction
55(1)
6.2 The Diffusion Propagator
56(4)
6.3 Flow Through the Infinite Barrier
60(2)
6.4 Asymptotic Probability Distribution
62(1)
6.5 General Localization Conditions
62(1)
6.6 A Family of Analytical Solutions
63(1)
6.7 Stochastic Resonance in a Monostable Non-Harmonic Time-Dependent Potential
64(7)
7 Non-Markov Processes: Colored Noise Case 71(14)
7.1 Introduction
71(1)
7.2 Ornstein-Uhlenbeck Case
72(5)
7.3 The Stationary Distribution
77(2)
7.4 The Interpolating Scheme
79(6)
7.4.1 Stationary Distributions
82(3)
8 Non-Markov Processes: Non-Gaussian Case 85(10)
8.1 Introduction
85(1)
8.2 Non-Gaussian Process η
86(3)
8.3 Effective Markov Approximation
89(6)
9 Non-Markov Processes: Nonlinear Cases 95(8)
9.1 Introduction
95(1)
9.2 Nonlinear Noise
96(4)
9.2.1 Polynomial Noise
96(2)
9.2.2 Exponential Noise
98(2)
9.3 Kramers Problem
100(3)
10 Fractional Diffusion Process 103(16)
10.1 Short Introduction to Fractional Brownian Motion
103(2)
10.2 Fractional Brownian Motion: A Path Integral Approach
105(3)
10.3 Fractional Brownian Motion: The Kinetic Equation
108(1)
10.4 Fractional Brownian Motion: Some Extensions
109(3)
10.4.1 Case 1
109(2)
10.4.2 Case 2
111(1)
10.5 Fractional Levy Motion: Path Integral Approach
112(4)
10.5.1 Gaussian Test
114(1)
10.5.2 Kinetic Equation
115(1)
10.6 Fractional Levy Motion: Final Comments
116(3)
11 Feynman-Kac Formula, the Influence Functional 119(10)
11.1 Feynman-Kac formula
119(4)
11.2 Influence Functional: Elimination of Irrelevant Variables
123(4)
11.2.1 Example: Colored Noise
126(1)
11.2.2 Example: Lotka-Volterra Model
127(1)
11.3 Kramers Problem
127(2)
12 Other Diffusion-Like Problems 129(12)
12.1 Diffusion in Shear Flows
129(4)
12.2 Diffusion Controlled Reactions
133(12)
12.2.1 The Model
133(3)
12.2.2 Point of View of Path Integrals
136(2)
12.2.3 Results for the Reaction A + B -> B
138(3)
13 What was Left Out 141(4)
Appendix A Space-Time Transformation: Definitions and Solutions 145(2)
A.1 Definitions
145(1)
A.2 Solutions
146(1)
Appendix B Basics Definitions in Fractional Calculus 147(2)
Bibliography 149(8)
Index 157