Physics 6810 "Computational Physics"
Spring, 2017

General Information about 6810 Computational Physics

Course title:

Computational Physics

Main References:

There is no required text to buy 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 2015 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 FYS4411.) Readings from these notes will be assigned on the course webpage as we proceed.
• You can download A Survey of Computational Physics by Rubin Landau, Manual Paez, and Cristian Bordeianu, which is an eTextBook using Python. It is a useful guide to the material we cover and a good source of projects. A related source is Computational Physics: Problem Solving with Computers by the same authors. The first edition of this text was used in the past, and 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 to be a good reference.
• Numerical Recipes: The Art of Scientific Computing by Press et al. (3nd edition) is a good first guide to algorithms and the old 2nd edition is available in online editions. [Local version of chapters now available with password.]
• 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 (Safari) by Lutz.
• There are many good C++ references to choose from if you want to supplement the class. This list from stackoverflow.com is organized according to your experience level. Here are a few that are available online for OSU people from the Safari page (the quoted descriptions are from the stackoverflow link):
  • Programming: Principles and Practice Using C++ (2nd Edition) by Bjarne Stroustrup [Safari link]. "An introduction to programming using C++ by the creator of the language. A good read, that assumes no previous programming experience, but is not only for beginners."
  • C++ Primer by Stanley Lippman, Josée Lajoie, and Barbara E. Moo [Safari link]. "An introduction to programming using C++ by the creator of the language. A good read, that assumes no previous programming experience, but is not only for beginners."
  • A Tour of C++ by Bjarne Stroustrup [Safari link]. "The 'tour' is a quick (about 180 pages and 14 chapters) tutorial overview of all of standard C++ (language and standard library, and using C++11) at a moderately high level for people who already know C++ or at least are experienced programmers. This book is an extended version of the material that constitutes Chapters 2-5 of The C++ Programming Language, 4th edition."
  • Effective C++: 55 Specific Ways to Improve Your Programs and Designs (3rd Edition) by Scott Meyers [Safari link]. "This is basically the new version of Effective C++, aimed at C++ programmers making the transition from C++03 to C++11 and C++14."
  • Effective Modern C++: 42 Specific Ways to Improve Your Use of C++11 and C++14 by Scott Meyers [Safari link]. "This was written with the aim of being the best second book C++ programmers should read, and it succeeded. Earlier editions were aimed at programmers coming from C, the third edition changes this and targets programmers coming from languages like Java. It presents ~50 easy-to-remember rules of thumb along with their rationale in a very accessible (and enjoyable) style. For C++11 and C++14 the examples and a few issues are outdated and Effective Modern C++ should be preferred."
• 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 few hits). They provide a compact C++ Language Tutorial that is very accessible. Or try their online tutorial.

Prerequisites:

The prerequisites are simply physics including some quantum mechanics. It will be expected that you 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 (PRB M2048) if you're concerned about your preparation (e.g., if you have very limited experience).

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. You can choose which to use. You can also use your own laptop (Linux, Windows, or Mac) if set up like the course computers. The wireless signal in the room is strong.
• 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 Eclipse IDE.
• We'll have the INTEL compilers (for C++ and Fortran 90/95) available on Linux.
• 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 xmgrace on Linux.)
• The GSL ("Gnu Scientific Library") is a free numerical library.
• Python is available at the command line in Cygwin or Linux, and there are stand-alone versions on both.
• MATLAB and Mathematica are available on all platforms for registered OSU students.

Lead 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 Instructor:

Bryan Smith
office: PRB 3000
email: smith.10851@osu.edu

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 WF from 12:40pm to 2:45pm 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)
[to be announced] (Furnstahl)
[to be announced] (TBA)

Grading:

In-class worksheets [30%]

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

Project [30%]

Web Pages:

This info: http://www.physics.ohio-state.edu/~ntg/6810/compphys_info.php

Course home page: http://www.physics.ohio-state.edu/~ntg/6810/compphys.php