Schedule of Classes

  • Lecture: Mon, Wed, Fri. 11:00 AM - 11:50 AM, Podemos 1A23

  • Discussion session: Wed. 7:00 PM - 7:50 PM, Podemos 0133

  • Instructor: Yi-Zhuang You (尤亦庄) (sounds like EACH-ONE, YOU) (He/Him/His)

  • Teaching Assistants: Wanda Hou. Email: hwanda@ucsd.edu

  • Course Grade Allocation:
    • Homework: 70%
    • Mid-term Exam: 10%
    • Final Exam: 20%
  • Assignment and Submission Platform:
    • Homework and exams are online. They will all be assigned and submitted via the UCSD Canvas platform.

  • Homework Guidelines:

    • Assignments will occur irregularly, depending on when the homework problem is encountered in the lecture.

    • A recommended deadline will be provided for each assignment. However, there are no penalties for late submissions (meaning that submissions are accepted until the end of the quarter). But please kindly manage your submissions to alleviate the TA’s workload. The hard deadline for all homework is by the end of the final week.

    • For each assignement, you have unlimited oppotunities to improve and resubmit your, even after your previous submission has been graded. The grade of your last submission will overide previous grades.

    • For homework, discussion among students and consulting with the TA are allowed. You may utilize all educational resources (including AI language models). Nevertheless, you are required to complete your assignments by yourself to maintain academic integrity. You are encouraged to acknowledge in your homework the resources that are helpful to you.

  • Examination Policies:

    • The scheduling for the mid-term and final exams has yet to be determined. Your input regarding preferred time slots is welcome.

    • Exams will be announced online, and you will typically be given a two-day window for completion. Extensions may be granted under exceptional circumstances.

    • All exams are take-home and open-book in nature. You are permitted to consult textbooks and lecture notes, employ computational tools, browse online resources, and interact with AI language models. However, discussing the exam content with other humans is not allowed.

Textbooks & References:

The lecture will be self-content, adapted from the following materials:

[1] David Tong, Statistical Physics. (PDF)

[2] Mehran Kardar, Statistical Physics of Particles.

[3] Nigel Goldenfeld, Lectures On Phase Transitions And The Renormalization Group.

Lecture Notes

As a UCSD student, you have free access to Mathematica (see how).

Part I. Statistical Ensembles (Mathematica) (PDF)

  • Information Theory
    • Probability Theory
    • Information and Entropy
    • Statistical Inference
  • Statistical Ensembles
    • Microcanonical Ensemble
    • Canonical Ensemble
    • Grand Canonical Ensemble
    • Summary

Part II. Quantum Gases (Mathematica) (PDF)

  • Thermal Quantum Gases
    • Bosons and Fermions
    • Statistical Distributions
    • Continuum Limit
    • Thermodynamic Properties
  • Degenerated Quantum Gases
    • Degenerated Bose Gas
    • Black-Body Radiation
    • Degenerated Fermi Gas

Part III. Phase Transitions (Mathematica) (PDF)

  • Liquid-Gas Transition
    • Phases and Phase Transitions
    • Lattice Gas Model
    • Markov Chain Monte Carlo
  • Magnetic Transition
    • Ising Model
    • Mean Field Theory
    • Landau Theory of Phase Transition
  • Renormalization Group
    • Overview
    • Representation Learning
    • Coarse Graining
    • Renomalization Group Flow
    • Universal Scaling Behaviors
    • Critical Exponent Estimations