PHYS 8601

Course objective:

A student will be expected to have developed an understanding the fundamental aspects of Monte Carlo sampling techniques and as well as substantial, practical problem-solving ability.

Text:

A Guide to Monte Carlo Methods in Statistical Physics, 2^nd Edition, D. P. Landau and K. Binder (Cambridge U. Press, 2005)

Topical outline for the course:

1. Introduction (philosophy)

2. Some background (statistical mechanics, thermodynamics, random number generation)

3. Comments on programming

4. Simple sampling Monte Carlo methods

5. Importance sampling Monte Carlo methods

6. Advanced Monte Carlo methods and techniques of analysis

7. Reweighting methods

8. Quantum Monte Carlo methods

9. Monte Carlo renormalization group methods

10. Simulations of non-equilibrium and irreversible processes

11. Introduction to other simulation methods

Projects

Students will be required to complete seven different projects based upon material presented in lectures and in the text. For each project students will have to implement an algorithm in Fortran or C, debug and run the code, analyze the results, and submit a report which includes the answers to questions posed in the assignment.

Grading Policy

Grades will be determined by the cumulative grade for the projects.