• In Matlab/octave you may need to install into your own filespace packages that you want to use, if they are not provided by the system-wide installation. Here are a few useful commands:

  pkg list

  list all available packages

  pkg install -forge xyz

  install the current version of the package from the standard repository

  pkg install xyz.1.2.3.tgz

  install a particular version of the package, pre-downloaded from https://gnu-octave.github.io/packages/. Sometimes, your octave installation may not be compatible with the latest version available; in this case, search and download the version that is compatible, by following the Version History link that shows all available versions and their compatibility.

  pkg load xyz

  once installed, a package needs to be loaded into your current workspace. This command needs to be issued every time, for packages installed in your own filespace. In contrast, the installation (see above) need to be done only once. It might be convenient to install packages from a full octave run, without performing these one-time steps in your jupyter notebooks.

What Brock calendar entry says (slightly revised for the current calendar):

• Survey of computational methods and techniques commonly used in condensed matter physics research; graphing and visualization of data; elements of programming and programming style; use of subroutine libraries; common numerical tasks; symbolic computing systems. Case studies from various areas of computational physics. Discipline-specific scientific writing and preparation of documents and presentations.

What do I need to bring into the course?

• This course is a core course of the MSMP program, and is recommended to all Physics graduate students. Basic familiarity with Linux is assumed, and will be reviewed briefly.

Course Goals

• to develop a working knowledge of interactions with Linux OS
• to become proficient in experimental data graphing and numerical analysis
• to gain working knowledge of program development
• to become familiar with one or several interpreted programming environments, and to acquire basic scripting skills
• to become proficient in LaTeX and use it for scientific papers and presentations

Textbook

There is no formal textbook for the course. A number of online resources are available and can be used as reference material for the lectures. Some of these are:

• Computational Physics by Morten Hjorth-Jensen (a local copy)(a tar file of programs from the text)
• Scientific Computing: An Introductory Survey by Michael T. Heath
• A Survey of Computational Physics by Rubin H. Landau, Manuel Jose Paez and Cristian C. Bordeianu (a local copy)
• Scientific Computing by Jeffrey R. Chasnov (a local copy)
• Principles of Scientific Computing by David Bindel and Jonathan Goodman (a local copy)
• An Introduction to Computer Simulation Methods Third Edition by Harvey Gould, Jan Tobochnik, and Wolfgang Christian (a local copy)
• Numerical Recipes by William H. Press, Saul A. Teukolsky, William T. Vetterling, and Brian P. Flannery (a local copy)
• Introduction to Programming with Fortran by Ian Chivers and Jane Sleightholme (a local copy)
• Numerical Methods and Computing by Ward Cheney and David Kincaid

This list will grow as the course progresses.

Component	% of the final mark	Notes
Projects	70%	Six (or seven, time permitting) extended projects. Project submissions should be made in the form of a jupyter notebook file, via email to the instructor.
Final presentation	30%	One of the projects (selected at random) submitted in the form of a scientific journal submission, using Phys. Rev. or Can. J. Phys. format, and presented in-class as a research seminar.

This is an approximate outline. Topics not on this list may get covered as time permits.

• A common toolbox
  • interacting with the OS; CLI vs GUI
  • Linux as a collection of small tools + pipes between them
  • the basics of programming: shell, C, scripting languages
  • code development: edit, compile, run, make and Makefile structure, elementary debugging, linking to program libraries
  • visualization with gnuplot and other graphing tools

• Numerical methods
  • Numerical differentiation: finite differences, interpolation, root finding
  • Numerical integration: special functions and quadrature
  • Solution of Ordinary Differential Equations: Euler-Cromer, Runge-Kutta
  • Linear algebra: methods of solving systems of equations and eigenvalue problems
  • Stochastic Methods:Random number generators, importance sampling, Molecular dynamics, Monte-Carlo techniques

• Case Studies/Projects
  • Least-squares problem: experimental noisy data, noise distribution and filtering, importance of baseline, assumption of Gaussian noise, chi-squared; classification of LS problems, regularization and selection of lambda. Test case: a spectrum of exponential relaxation rates
  • Molecular modeling: protein database data, force fields, GROMACS simulation package. Test case: a lipid bilayer with cholesterol guests (T.Harroun)
  • Models of solids: Ising model, magnetic moment, temperature, heat capacity, Metropolis' algorithm. Test case: magnetic transitions in an Ising lattice
  • Image Analysis: filtering to remove noise, segmentation to isolate regions and objects of interest, regional and spectral analysis to extract statistical data. Test case: spatial frequency distribution of a line image