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MPhys Physics

Year of entry: 2021

Course unit details:
Advanced Statistical Physics

Unit code PHYS40571
Credit rating 10
Unit level Level 4
Teaching period(s) Semester 1
Offered by Department of Physics & Astronomy
Available as a free choice unit? No

Overview

Advanced Statistical Physics (M)

Pre/co-requisites

Unit title Unit code Requirement type Description
Introduction to Quantum Mechanics PHYS20101 Pre-Requisite Compulsory
Statistical Mechanics PHYS20352 Pre-Requisite Compulsory

Aims

To understand the nature and scope of the dynamical description of the macroscopic world based on statistical principles.

Learning outcomes

This course unit detail provides the framework for delivery in 20/21 and may be subject to change due to any additional Covid-19 impact.  Please see Blackboard / course unit related emails for any further updates

On completion successful students will:

  1. Be able to explain what a Markov process is and to use analytical methods to study the dynamics of Markovian systems.
  2. Understand the origin of the irreversibility seen at the macroscale including examples which illustrate the essential ideas behind the fluctuation-dissipation theorem; be familiar with modern concepts relating equilibrium and non-equilibrium statistical physics. Bbe able to show how different kinds of description of stochastic processes are related, especially the idea of a microscopic model and its relation to a macroscopic model.
  3. Be able to perform straightforward calculations for systems which are described by stochastic dynamics, determining stationary probability distributions from master or Fokker-Planck equations and correlation functions from Langevin equations.
  4. Be familiar with the basic numerical methods used to simulate stochastic dynamical systems.

Syllabus

  1. Stochastic variables and stochastic processes

Revision of the basic ideas of probability theory; probability distribution functions; moments and cumulants; characteristic functions; the central limit theorem and the law of large numbers.

 

  1. Markov processes

The Chapman-Kolmogorov equation; Markov chains; Applications: (random walk, birth-death process); the master equation; methods of solution of the master equation; efficient simulation methods for Markov processes with discrete states.

 

  1. Drift and diffusion

The Fokker-Planck equation: derivation and methods of solution; relation to Schrödinger’s equation; applications to barrier crossing, activation and mean-first-passage times.

 

  1. Stochastic differential equations

The Langevin equation and its generalisations; analytical and numerical methods of solution; applications to Brownian motion.

 

  1. Modern topics in statistical physics

Fluctuation theorems; statistical physics of small systems; applications to complex systems modelling.

Assessment methods

Method Weight
Written exam 100%

Feedback methods

Feedback will be available on any students’ request.

Recommended reading

Gardiner, C. Stochastic Methods, A Handbook for the Natural and Social Sciences (Springer)
Jacobs, K. Stochastic Processes for Physicists, Understanding Noisy Systems (Cambridge University Press)
Reichl, L.E. A Modern Course in Statistical Physics, 2nd ed, (Wiley)

Study hours

Scheduled activity hours
Assessment written exam 1.5
Lectures 24
Independent study hours
Independent study 74.5

Teaching staff

Staff member Role
Alexander Grigorenko Unit coordinator

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