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Course Information

Course Information#

PHYSICS 211, EQUILIBRIUM STATISTICAL PHYSICS, SPRING 2024#

UNIVERSITY OF CALIFORNIA, BERKELEY DEPARTMENT OF PHYSICS

Instructor: Prof. Oskar Hallatschek (he/his), Office: 408C Stanley Hall, Email: ohallats@berkeley.edu

Teaching assistant: Irian D’Andrea (she/her), Email: irian@berkeley.edu

Lecture info: Tu, Th 3:30 - 5:00PM, Room: 105 - North Gate.

I’ll lecture live and post a recording to bCourses afterwards for those who can’t make it. If you have a question you’d like answered, but can’t make it to the lecture, send a note and I’ll answer it at the beginning of lecture and you can catch my answer on the recording.

Discussion sections: will be a mixture of problem solving, python computer experiments and interesting stat-mech applications.
W 5:00PM - 6:00PM, Room: B56 - Hidebrand
F 3:00PM - 3:59PM, Room: 109 - Dwinelle

Office hours:
Prof. H: Tu 5:00 - 6:00PM in 408C Stanley Hall
Irian: W 2:00 - 4:00PM in 420J Physics

Drop Deadline: Feb 10.

Course Webpage: here bcourses.berkeley.edu

Prerequisites: Physics 112, 137A/B or equivalents.

Texts:

  • M. Kardar, Statistical Physics of Particles. The paper copy is nice, but UC Library has an online version. This is the primary resource for the course.

  • D. Arovas, Thermodynamics and Statistical Mechanics. You can find Dan Arovas’ stat-mech notes on his UCSD page

  • J. Sethna, Entropy, Order Parameters, and Complexity. The 2nd edition, which I’ll use, is available online here

  • MacKay, Information Theory has lots of interesting connections between statistical physics and information theory.

  • I’ll provide reading guidelines at the beginning of each chapter.

Lecture notes: I’ll develop my own lecture notes as a jupyter book, which allows me to include Python code. You will be able to use Google Colab (merely requires a Google account) to open any page of the jupyter book, and play with the content, for example, to run variations of the code.

These notes will almost certainly have to go through various editorial iterations until they reach a steady state. Therefore, please send me info on typos or other kinds of suggested edits. You could do this, for example, by opening any page on Google Colab, modify the jupyter notebook (text, references, code etc.) and send the modified notebook myway.

The notes are hosted on github pages at https://hallatscheklab.github.io/StatPhys/intro.html.

Exams and grades: There will be one midterm and a final exam. Both will be 24-hour take home exams on the dates below.

  • Midterm, March 14

  • Final, Fri, May 10, 7PM - 9PM

Grades will be determined from a weighting of all the elements of the course as follows:

  • Midterm, 20 %

  • Final, 40 %

  • Homework, 40 %

  • extra credit: participation, 5 %

Homework: Homework will be posted approximately weekly and due Fridays at 5:00PM via Gradescope.

Late / missed homework policy: We will drop your lowest homework score. We will not accept late homework submission unless there are extenuating circumstances (e.g. sickness, family emergencies, natural desasters, accidents but also prepping for March meeting). If you expect to be ill or unable to do homework for more than a week, let us know.

Conflicts: Let me know of any exam conflicts at least two weeks before the exam. If circumstances make it impossible for you to take the midterm, we’ll shift its weight to the final.

Accommodations: If you need disability-related accommodations in this class, if you have emergency medical information you wish to share with the instructor, or if you need special arrangements in case the building must be evacuated should we return to campus, please inform Prof. Hallatschek immediately.

For your moral compass:
Collaboration and Independence: Reflecting the collaborative nature of science, I expect you to work on the problem sets together. But if you turn a problem in you should feel comfortable in your ability to explain it to me at the blackboard. Do not use online solution sets.

Cheating: Anyone caught cheating on an exam in this course will receive a failing grade on the relevant exam prob- lem(s), and will also be reported to the University Center for Student Conduct.

Plagiarism: To copy text or ideas from another source without appropriate reference is plagiarism and will result in a failing grade for your assignment and usually further disciplinary action. This includes copying solutions from printed or online, published or unpublished sources. Translation for grad school: don’t use online solution sets, and it’s good practice to cite things.

All above provisions listed in the course info sheet are subject to change at the instructor’s discretion. Changes may happen to address problems and to improve the smooth running of the class and/or discussion sections.

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