PHY 256: Computational Physics
(Spring 2016)
Instructor: Alice Quillen, Bausch and Lomb Hall 424, Phone:275-9625,
Email:aquillen x*x pas.rochester.edu
Lectures:
MW 2:00-3:15 in B+L407. Bring your laptop!
There is no TI or TA for this class.
Office hours: None. However the instructor tends to be in her office
from 9-5 on weekdays. She will be pleased when students drop by to chat and is
happy to help debug code.
If you want to be sure that the instructor is available please propose some
convenient times via email.
Overview:
Our goal is to explore interesting and cool physics using computational
techniques and with python.
Emphasis will be placed on visualization to increase understanding in trendy
topics rather than on
surveying numerical methods, though numerical algorithms and accuracy
will be explored as motivated by topic. We will also look at some novel
numerical approaches because they are fun and interesting for their own sake.
Topics covered could include:
Chaotic maps and dynamics,
simulation of the physics of simple robots and mechanical structures,
Monte Carlo simulations of phase transitions,
Particle dynamics, finite differencing techniques for
integrating continuum/fluid systems, billiards, random walks,
percolation and diffusion limited aggregation, vibrations,
stochastic systems, quantum information, symbolic computation and other topics.
Prerequisites:
Calculus, basic linear algebra. Familiarity with introductory modern physics.
Ability to get python with matplotlib and pylab (scipy) running somewhere on a computer.
python 3.5 recommended as I am writing notebooks that will run in that version.
Course requirements:
- 8-10 problem sets. These will mostly involve programming in python.
- 1 final numerical project with a 5 page write-up and
involving writing some code to explore
a physics related setting of your choice.
Grading: problem sets 70%, projects 30%
Rules: You can talk about your assignments with your fellow students.
You should write and run your own code. Code longer than a few lines
copied from sources on the internet should be identified.
Solutions to problems should not be directly copied from other students.
References and resources:
There are numerous on-line resources for python and computational physics.
So we are not following a single textbook.
Here are links to some of my favorite on-line resources.