Physics 411: Computational Physics
Winter 2014

Time:Tuesday and Thursday, 10-11:30am
Room:1250 USB
Instructor:Mark Newman
Office:322 West Hall
Office hours:Mondays 1:30-3:30pm

Quick links:


The use of computers in physics has grown enormously over the last half century, to the point where they play a central role in virtually every new physics discovery. From particle physics and astrophysics to quantum computing and biophysics, calculations on computers have become an indispensable tool of scientists today.

This course gives an introduction to the solution of physics problems using computers. Assuming no previous computer programming experience, the course will introduce the basic ideas and programming skills of computational physics and students will develop their own computer software to solve problems in quantum mechanics, atomic physics, astrophysics, condensed matter, nonlinear dynamics and chaos, biophysics, and other areas.


Calculus, linear algebra, and physics up to 390 will be required to understand the material. No previous programming experience is required. The course will make use of the Python programming language, and will begin with an introduction to programming in Python.

The course will require you to do a fair amount of programming work on your own time. One of the advantages of the Python programming language is that it is available for free, which means that if you have a computer you can download a copy of Python and work on your own computer. You can find instructions for installing Python in Appendix A of the textbook or on the web here. Most people should install version 3 of Python, which is the most recent version, but you can also use version 2 if you wish. (Some Mac users, particular those with older Macs, have reported that version 2 works better with their computers.) If you use version 2, then for compatibility you need to include the following line at the start of each program you write:

from __future__ import division,print_function
Note that there are two underscore symbols before the word future and another two after it. For further details about Python versions, see Appendix B in the textbook, or look here.

If you have difficulty installing Python on your computer, technical support is available from the physics Computing Support office on the second floor of Randall Lab.

If you do not own your own computer or you do but you prefer not to use it, computers are also available in the Physics Department's Physics Authorized Users Lab (PAUL) on the second floor of Randall Lab (vertically above the Physics Help Room, which is on the first floor, and next to the Computing and Technology Support office). Both Windows and Mac computers are available in PAUL, but only the PCs have the Python programming language installed, so you should choose a PC if you're going to work there. PAUL also provides printers that you can use to print out material to be handed in. Even if you use your own computer for doing homework you can still use the PAUL printers for printing it.


There will be weekly graded problem sets consisting mainly of programming challenges. Classroom time will be spent primarily on learning the principles and techniques behind computational physics methods; the homeworks are your chance to put those principles into action. A typical exercise will describe a physics problem that can be solved using a method studied in class and ask you to write a program to solve it and present your results.

Collaboration is allowed when doing exercises, but you must write your own programs and turn in your own work. Direct copying from others is not allowed. You may use example programs from the textbook as a starting point for your work, but copying from any other source, including books or the Internet, is not allowed.

There will be four take-home midterms during the semester, which will consist of programming challenges similar to those on the homeworks. The exams differ from the homeworks in that collaboration is not allowed. All exam problems must be solved without consulting others or copying from any source, except again that you may use programs from the textbook as a starting point. The dates of the midterms are listed on the schedule below. There will be no other homework in the weeks when there are midterms. There will be no final exam for this course.

The course grade will be 40% on the homeworks and 15% on each of the midterms.

There will be reading assignments for each lecture. The assignments are listed on the schedule below. Students are expected to do the reading for each lecture in a timely manner.


The textbook for the course is Computational Physics, M. Newman (Createspace Independent Publishing, 2012, ISBN 1480145513). You want the "revised and expanded" edition (with the red cover). It's available at the campus bookstore or you can buy it from Amazon.

I know of no other textbooks on computational physics using Python, but there are several good books that make use of other languages. If you want a second opinion on a particular topic you might like to consult one of the following:

As a general reference for numerical methods I can also recommend the following:

Problem sets:

Programs and data files

There are a bunch of programs and data files that you'll need for doing the homework. I've assembled everything you'll need into a single zip file which you can download from here. You should grab a copy of this file and unzip it into a convenient folder on your computer, so that you have all the files ready when you need them. Throughout the textbook and in the homeworks, where it mentions files in the "on-line resources", this is what it is referring to. The same resources can also be found on the textbook web site here.

Other files:


Thursday, Jan. 9Orientation and Introduction to PythonChapter 1Info sheet
Tuesday, Jan. 14Basic programming2.1, 2.2Homework 1Homework 1 handed out
Thursday, Jan. 16Loops and lists2.3, 2.4
Tuesday, Jan. 21For-loops and functions2.5-2.7Homework 2Homework 1 due, Homework 2 handed out
Thursday, Jan. 23GraphicsChapter 3
Tuesday, Jan. 28No classInclement weather
Thursday, Jan. 30Speed and accuracyChapter 4Homework 3Homework 2 due, Homework 3 handed out
Tuesday, Feb. 4Numerical integration5.1, 5.2
Thursday, Feb. 6Adaptive integration methods5.3, 5.4
Tuesday, Feb. 11No class
Thursday, Feb. 13Gaussian quadrature5.5-5.9Homework 3 due, Midterm 1 handed out
Tuesday, Feb. 18Numerical differentiation5.10Homework 4Midterm 1 due, Homework 4 handed out
Thursday, Feb. 20Linear algebra6.1, 6.2
Tuesday, Feb. 25Nonlinear equations6.3Homework 5Homework 4 due, Homework 5 handed out
Thursday, Feb. 27Maxima and minima6.4
Mar. 4, Mar. 6No classWinter Break
Tuesday, Mar. 11Fourier methods7.1-7.3Homework 5 due, Midterm 2 handed out
Thursday, Mar. 13The fast Fourier transform7.4Midterm 2 due
Tuesday, Mar. 18Ordinary differential equations8.1-8.3Homework 6Homework 6 handed out
Thursday, Mar. 20Adaptive methods for ODEs8.4
Tuesday, Mar. 25Leapfrog and Bulirsch-Stoer methods8.5Homework 7Homework 6 due, Homework 7 handed out
Thursday, Mar. 27Boundary value problems8.6
Tuesday, Apr. 1Partial differential equations9.1Homework 7 due, Midterm 3 handed out
Thursday, Apr. 3Forward time methods9.2Midterm 3 due
Tuesday, Apr. 8Spectral methods9.3Homework 8Homework 8 handed out
Thursday, Apr. 10Random processes10.1
Tuesday, Apr. 15Monte Carlo integration10.2Homework 8 due
Thursday, Apr. 17Monte Carlo simulation10.3Midterm 4 handed out
Tuesday, Apr. 22Simulated annealing10.4Midterm 4 due

Mark Newman