General Information about 780.20 Computational Physics
- Course title:
- Computational Physics
- Main References:
- There is no required text but there will be readings for each
class session from
handouts passed out in class and background notes posted online.
We'll supplement these with readings
- The 2011 lecture notes
by Morten Hjorth-Jensen from the University
of Oslo. Prof. Hjorth-Jensen's philosophy of
teaching computational physics is similar to mine and he
covers similar topics.
(His course web pages:
Recommended readings from these notes will be listed on the course
webpage as we proceed.
Survey of Computational Physics By Rubin Landau,
Manual Paez, and Cristian Bordeianu is an eTextBook using Python.
It is a useful guide to the material we cover and a good
source of projects.
It is the descendent of
Physics: Problem Solving with Computers
by Rubin Landau and Manuel Paez, which is the text we've used in the past.
Many of the codes we'll explore originated
from this book.
- Other References:
- The prerequisites are simply physics at least through
the undergraduate 26x series (although some have successfully
taken the course concurrently with 262!). It will be useful but not necessary
to have some experience with one or more
of Mathematica, MATLAB, Python,
C, fortran, or C++. The teaching strategy is to give you computer
programs and have you run and then modify (or debug) them as you
follow along through worksheets.
Email or visit Prof. Furnstahl
if you're concerned about
your preparation (e.g., if you have no experience at all).
- We'll start with an overview based on the first
part of the Hjorth-Jensen lecture notes and then cover
from the rest of the notes plus topics based on the
instructors' latest prejudices
and class interest (the latter to be
In most cases the discussion will be framed by a physics topic
such as nonlinear oscillations (e.g., chaos).
We'll be using programs written in C++ and Python and occasionally
Matlab or Mathematica
as we go along.
Some topics we will cover along the way:
- Errors and uncertainties in computations. E.g.,
one should understand how to analyze whether a calculation
is limited by the algorithm or round-off error.
We will come back to this topic repeatedly.
- Basic computational algorithms for: integration, differentiation,
differential equations, root finding. Less emphasis on
theory than on understanding how well an algorithm
should work (e.g., should the accuracy improve as 1/N2,
where N is
the number of points used and does it?) and what algorithm is appropriate for what
situation (e.g., oscillatory integrals or integrands with
singularities). In many (or most)
cases you should be using a packaged library
routine and not writing your own, so we'll learn how to use such a
library and check the results.
- What you should know about: random numbers, Monte Carlo integration
and simulation, matrix computing, calling Fortran libraries
from C++, plus additional topics as time
- A survey of some advanced computational algorithms as we go.
- Aspects of writing code: good programming practices;
how to test and debug a code (C++, fortran, MATLAB, or whatever);
how to tune a code to run faster.
- Aspects of a computational physics project: breaking down a
project into sub-problems; implementation issues (e.g., program design,
code conventions, makefiles, using a scripting language); use of graphics for visualization;
validation/verification; using a version control system.
- Parallel processing: introduction to OpenMP and MPI.
- Object-oriented programming: What is it and when is it relevant for
- Using Mathematica or MATLAB for computational physics. This is a broad
topic, of course, and we will just touch upon aspects here.
- Computing Environment:
- The general idea is to use basic and portable tools.
The homepage will have details about setting these up on your
- The computers in Smith 1094 can be run with Linux or
Windows XP. You can choose which to use.
- For Linux users, the computer environment
include the GNU tools (also available in
Smith 1094). These include g++, make, indent, gdb, gprof,
and editors (e.g., emacs, nedit).
- For Windows users, the computing environment will be mainly
Cygwin, which simulates the GNU/Linux environment.
(You can also log into a public Linux
machine via an X-windows program, Xwin32.)
Sometimes we might use the Dev-C++ IDE.
- We'll have the INTEL compilers (for C++ and Fortran 90/95)
available on both platforms.
- We'll use gnuplot for plotting, from the command line in
Cygwin or Linux and also as a standalone program on Windows.
(Also Python and maybe xmgrace.)
- The GSL ("Gnu Scientific Library") is a free numerical library.
- Python is available at the command line in Cygwin or Linux,
and there is a stand-alone version on Windows.
- MATLAB and Mathematica are available on
all platforms for registered OSU students.
- Prof. Richard Furnstahl
office: M2048 PRB
email: firstname.lastname@example.org or email@example.com
phone: 292-4830 (office) or 847-4026 (home)
- 1094 Consultant:
office: 4124 PRB
phone: 292-2086 (office)
- Computer Consultant:
- Terry Bradley
office: 1199 PRB
phone: 292-8598 (PRB office) or 292-4269 (Stillman Hall)
- Class meets MW from 11:30am to 1:30pm
in Smith 1094.
Each period will primarily be a hands-on lab
session (after a short lecture/question part).
- Office Hours:
- By appointment (asking in class is easiest) and Fridays
[to be announced] (Furnstahl)
[to be announced] (Orban)
- In-class worksheets [30%]
- Assigned homework ("handed in" via Mercurial) [40%]
- Project [30%]
- Web Pages:
- This info:
- Course home page:
Your comments and
suggestions are appreciated.
[Math and Physical Sciences]
[Ohio State University]
Physics 780.20 Computational Physics Information.
Last modified: .