Path Integrals for Stochastic Processes : An Introduction.
Title:
Path Integrals for Stochastic Processes : An Introduction.
Author:
Wio, Horacio S.
ISBN:
9789814449045
Personal Author:
Physical Description:
1 online resource (174 pages)
Contents:
Contents -- Preface -- 1. Stochastic Processes: A Short Tour -- 1.1 Stochastic Process -- 1.2 Master Equation -- 1.3 Langevin Equation -- 1.4 Fokker-Planck Equation -- 1.5 Relation Between Langevin and Fokker-Planck Equations -- 2. The Path Integral for a Markov Stochastic Process -- 2.1 The Wiener Integral -- 2.2 The Path Integral for a General Markov Process -- 2.3 The Recovering of the Fokker-Planck Equation -- 2.4 Path Integrals in Phase Space -- 2.5 Generating Functional and Correlations -- 3. Generalized Path Expansion Scheme I -- 3.1 Expansion Around the Reference Path -- 3.2 Fluctuations Around the Reference Path -- 4. Space-Time Transformation I -- 4.1 Introduction -- 4.2 Simple Example -- 4.3 Fluctuation Theorems from Non-equilibrium Onsager- Machlup Theory -- 4.4 Brownian Particle in a Time-Dependent Harmonic Potential -- 4.5 Work Distribution Function -- 5. Generalized Path Expansion Scheme II -- 5.1 Path Expansion: Further Aspects -- 5.2 Examples -- 5.2.1 Ornstein-Uhlenbeck Problem -- 5.2.2 Simplified Prey-Predator Model -- 6. Space-Time Transformation II -- 6.1 Introduction -- 6.2 The Diffusion Propagator -- 6.3 Flow Through the Infinite Barrier -- 6.4 Asymptotic Probability Distribution -- 6.5 General Localization Conditions -- 6.6 A Family of Analytical Solutions -- 6.7 Stochastic Resonance in a Monostable Non-Harmonic Time-Dependent Potential -- 7. Non-Markov Processes: Colored Noise Case -- 7.1 Introduction -- 7.2 Ornstein-Uhlenbeck Case -- 7.3 The Stationary Distribution -- 7.4 The Interpolating Scheme -- 7.4.1 Stationary Distributions -- 8. Non-Markov Processes: Non-Gaussian Case -- 8.1 Introduction -- 8.2 Non-Gaussian Process η -- 8.3 Effective Markov Approximation -- 9. Non-Markov Processes: Nonlinear Cases -- 9.1 Introduction -- 9.2 Nonlinear Noise -- 9.2.1 Polynomial Noise -- 9.2.2 Exponential Noise -- 9.3 Kramers Problem.
10. Fractional Diffusion Process -- 10.1 Short Introduction to Fractional Brownian Motion -- 10.2 Fractional Brownian Motion: A Path Integral Approach -- 10.3 Fractional Brownian Motion: The Kinetic Equation -- 10.4 Fractional Brownian Motion: Some Extensions -- 10.4.1 Case 1 -- 10.4.2 Case 2 -- 10.5 Fractional Levy Motion: Path Integral Approach -- 10.5.1 Gaussian Test -- 10.5.2 Kinetic Equation -- 10.6 Fractional Levy Motion: Final Comments -- 11. Feynman-Kac Formula, the Influence Functional -- 11.1 Feynman-Kac formula -- 11.2 Influence Functional: Elimination of Irrelevant Variables -- 11.2.1 Example: Colored Noise -- 11.2.2 Example: Lotka-Volterra Model -- 11.3 Kramers Problem -- 12. Other Diffusion-Like Problems -- 12.1 Diffusion in Shear Flows -- 12.2 Diffusion Controlled Reactions -- 12.2.1 The Model -- 12.2.2 Point of View of Path Integrals -- 12.2.3 Results for the Reaction A + B → B -- 13. What was Left Out -- Appendix A Space-Time Transformation: Definitions and Solutions -- A.1 Definitions -- A.2 Solutions -- Appendix B Basics Definitions in Fractional Calculus -- Bibliography -- Index.
Abstract:
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.
Local Note:
Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2017. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.
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Electronic Access:
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