Advanced course on Computational Physics for Ph.D. and MS students (Winter-Spring 2023)
This course is devoted to advanced and more recent topics in computational methods for physics.
Link for my previous lectures on Computational Physics (SBU-VPN needed)
Some topics to teach are as follows:
- Solving coupled Differential Equations and Boundary Value Problems
- Chaotic phenomena
- Probability Distribution functions and transformations
- Correlation functions, Two-point correlation function
- Spectral analysis
- Monte Carlo simulation
- Basic topics for Molecular dynamics simulations
- Simulation by VPython
- Machine learning in Physics
- Topological Based Data Analysis
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Course subjects and program (Download)
- A good movie presented by Pooyan Goodarzi to connect the server, remotely (Link)
- Computational Physics By RUBIN H. LANDAU, MANUEL JOSE PAEZ and CRISTIAN C. BORDEIANU (See this link)
- A good presentation by Kip R. Irvine for number representation (Download)
- Some bash samples (Download)
A script for plotting figure by Python (Download)
- A good text for commands in Fortran, C++, Matlab (Download)
- VPython
- Some necessary things for programming skills (Download)
- A good paper for data analysis in cosmology by Licia Verde, arXiv:0712.3028
- Online numerical recipes (http://www.numerical.recipes)
- Computational physics course by Dr. Seyed Akbar Jafari, Sharif University of Technology
- A good note prepared by Dr. Seyed Akbar Jafari (Download)
- Note on Quantum Monte Carlo by Dr. Mehdi Neek Amal (Download)
- My lecture concerning Errors and PDF (Download) (Download)
- Some of my Python programs (Download)
- Visualization by Matlab (link)
- Discretization approaches (Download)
- My note about deterministic Fractals (Download) & (See this link)
- A good reference for errors https://archive.org/details/TaylorJ.R.IntroductionToErrorAnalysis2ed/page/n149
- A proper series for Machine learning (Part 1), (part 2),
- Some good Books for Machine learning and related topics, http://www.aghamousa.com/data-science-books/
- A pedagogical link for MCMC code

Some of my lectures on the Board
My previous lectures (Link) and (Link) and (Link)
- Preliminary Part (Download)
- Number Representation (Download) (Download)
- Data Science (Download)
- Error estimation (error estimation) my note included (error estimation)
- TDA (Download)
A script for plotting figure by Python (Download)
Exams timeline
First midterm will be held on 18 Esfand 1401 at 10 a.m. (Questions and Data)
شماره دانشجویی |
midterm1-70 |
401416009 |
0 |
401416010 |
48 |
401516005 |
50 |
400416019 |
0 |
401416025 |
51 |
401416029 |
0 |
401416042 |
53 |
401416044 |
55 |
401416078 |
57 |
401416079 |
42.5 |
401416054 |
0 |
400416046 |
0 |
401416059 |
59 |
401416082 |
52 |
401416083 |
59 |
401416067 |
55 |
Exercises:
# Set 1 (Download) Necessary files (Q1-Part A) (Q1-Part B)
# Set 2 (Download) Necessary files (x_data and y_data and Data) (Datatypes.pdf)
# Set 3 (Download) Necessary files (Data)
# Set 4 (Download) Necessary files (FGN & FBM & Part E)
# Set 5 (Download) Necessary files (Q1 & Q2 Data package (Download); Q3 (Download); Q8: data1 (Download); Q9-12 (Download) (data including 0.2.txt, 0.5.txt and 0.8.txt which are same as data for Q1 & Q2)
# Set 6 (Download) Necessary files ((1+1)-Dimensional data (Download) & (1+2)-Dimensional data (Download) & (Download))