Physics 780.20 "Computational Physics"
Winter, 2012

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 from:

• The 2011 lecture notes [pdf] 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: FYS3150 and FYS4410.) Recommended readings from these notes will be listed on the course webpage as we proceed.
• A 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 Computational 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:

• For more about C++ object-oriented programming, look at A First Course in Computational Physics and Object-Oriented Programming with C++ by David Yevick [contents and excerpts are available on Amazon.com] and C++ and Object-oriented Numeric Computing for Scientists and Engineers by Daoqi Yang.
• I've found Writing Scientific Software: A Guide to Good Style by Oliveira and Stewart, which is available from Amazon.com for only about $10 used, to be a good reference.
• Numerical Recipes: The Art of Scientific Computing by Press et al. (2nd edition) is available in online editions (there is a 3rd edition but it is not freely available).
• Python Scripting for Computational Science by Langtangen is a good reference for the basics of Python and what you need to know to do computational physics with it. The link is to the OSU E-book version, from which you can get PDF's of individual chapters. A good (non-computational) introduction to Python is Learning Python by Lutz.
• There are many good C++ references to choose from if you want to supplement the class. Here are a few that are available online for OSU people from the Safari page.
  • Teach Yourself C++ in 21 Days by J. Liberty and B.L. Jones [Safari link]. The title sounds a bit goofy but the order of topics and the pacing is well suited for learning C++ quickly.
  • C++ Primer Plus by Stephen Prata [Safari link]. This has very good reviews, but works better as a reference than a short-term learning guide.
  • Practical C++ Programming by Steve Oualline (published by O'Reilly) [Safari link]. This has a lot of good advice for writing C++ programs.
• Another excellent resource for C++ programming is http://www.cplusplus.com (if you Google a C++ command, this is likely to be one of the first hits).

Prerequisites:

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).

Material:

We'll start with an overview based on the first part of the Hjorth-Jensen lecture notes and then cover selections from the rest of the notes plus topics based on the instructors' latest prejudices and class interest (the latter to be determined!). 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 permits.
• 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 computational problems?
• 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 personal computer.

• 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.

Instructor:

Prof. Richard Furnstahl
office: M2048 PRB
email: furnstahl.1@osu.edu or furnstah@mps.ohio-state.edu
phone: 292-4830 (office) or 847-4026 (home)

1094 Consultant:

Chris Orban
office: 4124 PRB
email: orban.14@osu.edu
phone: 292-2086 (office)

Computer Consultant:

Terry Bradley
office: 1199 PRB
email: bradleyt@mps.ohio-state.edu
phone: 292-8598 (PRB office) or 292-4269 (Stillman Hall)

Schedule:

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)

Grading:

In-class worksheets [30%]

Assigned homework ("handed in" via Mercurial) [40%]

Project [30%]

Web Pages:

This info: http://www.physics.ohio-state.edu/~ntg/780/compphys_info.php

Course home page: http://www.physics.ohio-state.edu/~ntg/780/compphys.php