Statistical Physics (2024)

Statistical mechanics enables us to model the behavior of macroscopic objects, which are made up of large numbers of constituents for which we only have incomplete descriptions, using probability theory and a microscopic theory of the constituents. It bridges the disconnect between mechanics which require complete knowledge of initial conditions, and the real world where such information is not available. These ideas therefore find widespread application.

Target Audience

This is course SP-1 in the TIFR graduate school.

This page will be updated regularly with course-related information. Please check frequently.

Administrivia

Time: Tu, Th at 930 hrs

Venue: AG 69

First lecture: 6 Feb

Credit policy: Tentatively Midterm (35%) + Endterm (35%) + Assignment (30%)

Instructor: Basudeb Dasgupta

Tutors: Sandeep Jangid and Soumya Pal

Course Webpage: Moodle [TIFR only]

Lecture Notes: Dropbox Link [Public]

Course Contents

1. Preliminaries: Motivation and review of thermodynamics

2. Probability and statistics: Counting, distributions, large numbers

3. Kinetic theory and approach to equilibrium; Brownian walks etc.

4. Classical statistical mechanics: Formalism and simple systems

5. Classical statistical mechanics: Interactions, approximations, phase transitions

6. Quantum statistical mechanics: Formalism, ideal Bose/Fermi gases, phase transitions

7. What else is there? and Review of the course

References

1. Statistical Mechanics, Huang (I personally find it most readable)

2. Statistical Physics (Berkeley Physics Course Vol.5), Reif

3. Statistical Mechanics of Particles, Kardar (Main text, but very terse. Videos are awesome.)

4. Statistical Mechanics Part-I (Course of Theoretical Physics Vol.5), Landau and Lifshitz

5. Lecture notes by David Tong, Cambridge Univ. (Kinetic Theory, Statistical Mechanics)

Problem Sets

1. PS1 (thermodynamics and probability)

2. PS2 (kinetic theory)

3. PS3 (classical stat. mech + interactions)

4. PS4 (quantum stat. mech)

Exams

1. Droptest on 18 Feb

2. Midterm (4 April, AG69)

3. Endterm (30 May, 9am-1pm, AG69)

Lecture Summaries (tentative)

 Lecture 1 (Feb 6)

Lecture 2 (Feb 8)

Lecture 3 (Feb 13)

Lecture 4 (Feb 15)

Lecture 5 (20 Feb)

Lecture 6 (Feb 22)

Lecture 7 (Feb 27)

Lecture 8 (Feb 29)

Lecture 9 (Mar 5)

Lecture 10 (Mar 7)

Lecture 11 (Mar 12)

Lecture 12 (Mar 14)

Lecture 13 (Mar 19)

Lecture 14 (Mar 21)

Lecture 15 (Mar 26)

Lecture x (Mar 28)

Midterm Week (1-5 April)

Lecture x (April 9)

Lecture x (April 11)

Lecture 16, 17, 18, 19, 20 (April 16, 18, 23, 30, May 2)

Lectures 21, 22 (April 22 at 4pm, 25 at 10am)

Lectures 23 onwards (May 7, 9, 14, 16, 21, 23)