• Final project presentations will take place Monday, Dec. 15, starting at 09:00:
  • 09:00 ss22qo (Sam)
  • 09:30 jk24ot (Jianhua)
  • 10:00 sd25nr (Jose)
  • 10:30 gu22wo (Genevieve)
  • 11:30 nq24lx (Anish)
  • 11:00 zx24gq (Amandeep)
  • 12:00 jm21vg (Justus)
  • 12:30 sz24pg (Liu)

  A full program of presentations is available here.

  Written submissions of final projects are due no later than noon on Friday, Dec.19. The expected format is that of a scientific paper.

  BrockU templates for both a slide presentation and the written submission have been placed in file:///work/5P10, but copying this link into your browser will only work on the computers in the Physics cluster.

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 computing with Matlab/octave and similar environments

• 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
  • Atmospheric temperatures: extracting and validating data from external sources, dataframe creation and management, regression analysis, identification and handling of confounding influencers, computing correlations
  • Models of solids: Ising model, magnetic moment, temperature, heat capacity, Monte-Carlo techniques, 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