Left: Simulation of a bacteria growth by DLA method, Middle: Molecular dynamics simulation of a small system of atoms
Right: Band structure of a heavy fermion material.
This course is a continuation of Computational Physics course
(509). It introduces andvanced concepts and algorithms in
Computational Condensed Matter Physics and brings students to
the active research in Computational Condensed Matter area.
Lectures will be given in "hands on" style only, and students
should bring their own laptops to follow the lectures. Latops
should run python (including numpy, scipy, weave,
matplotlib) and should have C++ and fortran compiler installed.
This course requires familiarity with some basics of
programming languages such as Python (and a little of C++).
It is designed for the student who wishes to
broaden his/her knowledge of applications of computation and develop
techniques in Computational Physics.
Class Time: ARC building (108), 3:20-4:40pm on Monday
Office: Serin E267
Phone: 445 5500, ext: 3881
Office hours: Monday 4 pm
If you are not yet familiar with Python, or you just want
to refresh your memory, check out some of these links:
- Learn Python in 10 minutes
- How to Think Like a Computer Scientist
- Python for beginners
- Dive Into
- Code Like a Pythonista: Idiomatic Python
- Python documentation
- Python regular expressions
- Weave (to speed up
the Python code)
Preliminary Course Outline and Tentative List of Topics include
- Set up the environment on your computer to be able to
code in Python or C++. The instructions from 509 can help.
- Perturbation theory at low orders
- Random numbers and multidimensional integration
- Monte Carlo methods and Simulated Annealing
- Parallel programming with MPI
- Exact diagonalization
- Quantum Monte Carlo methods
- Continuous Time Quantum Monte Carlo method
- Density functional theory
- Dynamical Mean Field Theory for model Hamiltonians
- Local Density Approximation + Dynamical Mean Field Theory (LDA+DMFT)
- Molecular dynamics simulation
Students with Disabilities