Statistical Physics (MATH327), Spring 2021
Probabilistic processes provide often-outstanding mathematical descriptions of systems within the domain of statistical physics. This course introduces the foundations of statistical physics, including the concepts of statistical ensembles, the laws of thermodynamics, and derived quantities such as entropy. These foundations will be applied to investigate diffusion, the behaviour of idealized physical systems such as classical and quantum gases, thermodynamical cycles, and phase transitions. In particular, computer simulations will be used to numerically investigate diffusion and transport in terms of stochastic processes.
Upon completing this module, students are able to:
- Demonstrate understanding of the micro-canonical, canonical and grand-canonical ensembles, their relation and the derived concepts of entropy, temperature and particle number density.
- Understand the derivation of the equation of state for non-interacting classical and quantum gases.
- Demonstrate numerical skills to understand diffusion from an underlying stochastic process.
- Know the laws of thermodynamics and demonstrate their application to thermodynamic cycles.
- Be aware of the effects of interactions, including an understanding of the origin of phase transitions.
This module is intended for third- and fourth-year undergraduates as well as students in the one-year taught MSc programme. Familiarity with combinatorics and quantum mechanics is not assumed. Any necessary information on those topics will be provided. All resources for the module will be gathered at its Canvas site.
These gapped lecture notes are the main learning resource. There is also a python programming demo illustrating all the tools that should be needed for the computer project. All of these are kept under version control at GitHub, providing an easy way to monitor any typo fixes or other improvements. Reports of any issues, and pull requests to address them, are also welcome. There will also be roughly 20 minutes of video recordings per week, to enhance the human element especially for those unable to join the twice-weekly synchronous sessions. Here is an introductory video summarizing the module logistics.
Synchronous sessions are timetabled at 9:00 on Mondays and 13:00 on Thursdays. Office hours will take place immediately afterwards, at 10:00 on Mondays and 14:00 on Thursdays. You can also make an appointment to speak with me at other times.
As you know by this point in your studies, the best way to learn mathematics is by doing mathematics. This includes (but is not limited to) making sure you can fill in the gaps in the lecture notes and solve the homework problems yourself (though I encourage you to discuss these topics with each other). Sample exams are also available, and I expect to design additional 'tutorial' exercises in response to reported difficulties, to offer further opportunities for practice and reinforcement. Should you ask me questions about these exercises and problems, I will endeavour to avoid doing the work for you; instead I will have you explain to me what you have tried so far, and will ask leading questions to suggest where I see problems or potential next steps.
In addition to the gapped lecture notes, potentially useful statistical physics references at roughly the level of this module include:
- David Tong, Lectures on Statistical Physics (2012)
- Daniel V. Schroeder, An Introduction to Thermal Physics (first edition, 1999)
- C. Kittel and H. Kroemer, Thermal Physics (second edition, 1980)
- F. Reif, Fundamentals of Statistical and Thermal Physics (first edition, 1965)
There are also many textbooks at a higher level than this module, including the following:
- R. K. Pathria, Statistical Mechanics (second edition, 1996)
- Sidney Redner, A Guide to First-Passage Processes (first edition, 2007)
- Pavel L. Krapivsky, Sidney Redner and Eli Ben-Naim, A Kinetic View of Statistical Physics (first edition, 2010)
- Kerson Huang, Statistical Mechanics (second edition, 1987)
- Weinan E, Tiejun Li and Eric Vanden-Eijnden, Applied Stochastic Analysis (first edition, 2019)
- Michael Plischke and Birger Bergersen, Equilibrium Statistical Physics (third edition, 2005)
- L. D. Landau and E. M. Lifshitz, Statistical Physics, Part 1 (third edition, 1980)
Finally, this general book about learning provides useful information about what strategies are most effective, for example retrieval practice compared to re-reading lecture notes or re-watching videos:
- Peter C. Brown, Henry L. Roediger III and Mark A. McDaniel, Make it Stick: The Science of Successful Learning (first edition, 2014). A short summary video is also available
Week 3 (22–28 February): Canonical ensemble
Intro and Wrap-up: The thermal reservoir; Replicas and occupation numbers; Partition function; Internal energy, heat capacity, and entropy; Helmholtz free energy; The physics of information (distinguishable vs. indistinguishable spins)
Week 4 (1–7 March): Ideal gases
Intro and Wrap-up: Volume, energy levels, and partition function; Internal energy, and entropy; The mixing entropy and the 'Gibbs paradox'; Pressure, ideal gas law, and equations of state
Week 6 (15–21 March): Grand-canonical ensemble
Part 1: Particle reservoir and chemical potential; Grand-canonical partition function
Part 2: Grand-canonical potential, internal energy, entropy and particle number
Week 8 (19–25 April): Quantum gases of bosons
Week 9 (26 April – 2 May): Quantum gases of fermions
Week 10 (3–9 May): Interacting systems
Week 11 (10–16 May): Synthesis and broader applications
Last modified 11 April 2021