Overview
Introductory lecture material
Programming
Basic numerical methods
Methods and Algorithms
Monte Carlo methods
Quantum Monte Carlo methods
Continuous Time Quantum Monte Carlo
HartreeFock method
Density functional theory
Molecular Dynamics


Left: Simulation of a bacteria growth by DLA method, Right: Molecular dynamics simulation of a small system of atoms
Simulation codes is available to download in lecture material.
This course introduces logarithmic concepts and familiarizes
students with the basic conputational tools which are essential
for graduate students in computational physics and related
areas.
In this course, students work toward mastering computational
skills, needed to work in classical and quantum physics using
the computer. Examples will be drawn from various areas of
physics.
As the programing language, we will use mostly Python and
its scientific library scipy & numpy.
To speed up parts of the code, we will also
use C++ and fortran90 for short examples, which will be used
through the Python interface.
This course has no prerequsites except for familiarity with some
programming language. It is designed for the student who wishes to
broaden his/her knowledge of applications and develop
techniques.
To follow the course more efficiently, and perform the hands on
training, it is desired that students bring their own laptops to
the class.
Class Time: ARC212 (203), 12:001:20pm on Wednesday, 1:403:00pm on Friday
Instructor: 
Kristjan Haule
Office: Serin E267
email: haule@physics.rutgers.edu
Phone: 4453881
Office hours: Friday 4:45pm

Homeworks
Preliminary Course Outline and Tentative List of Topics include
A)Introduction:
 System installation, Python instalation, libraries and environment.
 Comparison of modern programing languages.
 Representation of numbers in the computer.
 Why do we need to be aware of roundoff error.
 How to speed up the code with softwater such as f2py and weave.
 Optimizing your software with coding tricks, libraries, and profiliers
 How to use state of the are computer hardware. When to use GPU's
B) Learning high level programming
with examples in Python and C++:
 Modern Scientific Computing using Python, SciPy, Numpy, Pylab
 Importance of data abstraction, data encapsulation and data hiding
 Concept of templates and their use (functors)
 Be aware of 8020 rule in optimizing code
Literature for Pythons:
 Software carpentry

How to Think Like a Computer Scientist: Learning with Python
 Python for beginners
 Dive Into Python
 Python documentation
 Python regular expressions
Literature for C++:
 C++ Programming language by Bjarne Stroustrup
 More Effective C++ by Scott Meyers
 The ObjectOriented Numerics Page
C) Basic numerical methods:
 Numeric integration (source code)
 Interpolation, Splines and Fourier transformation (source code)
 Differential equations (source code)
 Random numbers and multidimensional integration (source code)
 Parallel programming with MPI ( source code)
Literature:
Numerical Recipes online from http://www.nrbook.com/b/bookcpdf.php
D) Computational methods and algorithms:
 Monte Carlo methods and Simulated Annealing
 HartreeFock method
 Density functional theory
 Quantum Monte Carlo methods
 Continuous Time Quantum Monte Carlo method
 Molecular dynamics simulation
Literature:
 Computational Physics by J.M. Thijssen
 Introduction to Computer Simulation Methods by H. Gould, J Tobocnik and W. Christian
 Electronic Structure, Basic Theory and Practical Methods by Richard M. Martin(Very good book for the Density functional part of the course)
 An Introduction to Computational Physics by Tao Pang
 Computational Physics by Rubin H. Landau and Manuel J. Paez(More elementary but good book)
Students with Disabilities
