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
- Course subjects and program (Download)
- A sample for Linux commands skills (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
Other related materials and coursesSome 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)
- Quantum Machine Learning (Part1 & Part2) By Narges Eghbali and Anahid Kiani (Film)
- Non-parametric modeling: Gaussian Processes (Download) By Ali Haghighatgoo
Exams timeline
First midterm will be held on 18 Esfand 1401 at 10 a.m. (Questions and Data)
Second midterm will be held on 21 Ordibehesht 1402 at 9:00 a.m. (Questions & Answer-key1 & Answer-key2 )
Final exam will be held on 31 Khordard 1402 at 9:00 a.m. (Questions & Data)
Final Mark (Download)
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))
# Set 7 (Download)
# Set 8 (Download) Necessary file (Download)
# Set 9 (Download) Necessary file (Download)
# Set 10 (Download)
# Set 11 (Download)
