Langevin Dynamics (Part 3) 
Langevin Dynamics (Part 3)
by IIT Madras
Video Lecture 14 of 29
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Date Added: April 3, 2016

Lecture Description

This video lecture, part of the series Physical Applications of Stochastic Processes by Prof. , does not currently have a detailed description and video lecture title. If you have watched this lecture and know what it is about, particularly what Physics topics are discussed, please help us by commenting on this video with your suggested description and title. Many thanks from,

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Course Index

Course Description

Probability and statistics: Joint and conditional probabilities and densities. Moments, cumulants, generating functions, characteristic function. Binomial, Poisson, Gaussian distributions. Stable distributions, limit theorems, diffusion limit of random flights. Infinitely divisible distributions.
Stochastic processes: Discrete and continuous random processes. Joint and conditional probability distributions. Autocorrelation function. Markov chains. Discrete Markov processes, master equation. Poisson process, birth-and-death processes. Jump processes. Correlation functions, power spectra. Campbell's Theorem, Carson's Theorem. Thermal, shot, Barkhausen and 1/f noise.
Continuous Markov processes: Chapman-Kolmogorov equation, transition rate, Kramers-Moyal expansion. Fokker-Planck equation, backward Kolmogorov equation, first passage and exit time problems. Level-crossing statistics.
Stochastic differential equations: Langevin equation, diffusion processes, Brownian motion, role of dimensionality, fractal properties.
Random walks: Markovian random walks. Random walks and electrical networks, random walks in biology. Levy flights. Self-avoiding walks and polymer dynamics. Random walks on fractals. Non-Markov continuous time random walks.
Randomness in deterministic dynamics: Coarse-grained dynamics, Markov and generating partitions, recurrence statistics.


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