Advanced course on Computational Physics for Ph.D. and MS students  (Winter-Spring 2021)

This course is devoted to advanced and more recent topics in computational methods for physics.

Link for class https://vc15.sbu.ac.ir/class-3992161229401

Link for my previous lectures on Computational Physics (SBU-VPN needed)

Link for my previous lectures on Computational Physics (In my homepage)

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

• 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 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 courses

• Researches methods course (Link)
• Critical Phenomena and Phase transitions (Link)
• Data Analysis workshop (Link)
• Data Sciences (Link)
• Challenges in training and researches in Physics (Link)

Some of my lectures on the Board

• Preliminary part (Download)
• Number Representation (Download) (Download)
• Data Science (Download)
• Error estimation (error estimation) my note included (error estimation)
• Lecture 5
• Lecture 5-1
• Lecture 5-2
• Lecture 1400/01/24  and see (fitting)
• Lecture 1400/01/31
• Lecture 1400/02/07
• Lecture 1400/02/14
• Lecture 1400/02/21
• Lecture 1400/02/28
• Lecture 1400/03/04
• Lecture 1400/03/11

For more details see my other lectures (Link)

Exercises:

#Set 1 (Download) List_arrange (Download)

#Set 2 (Download), data (Download)

#Set 3 (Download)

#Set 4 (Download) fitinput (Download), sin.txt (Download), Data package (Download)

#Set 5 (Download) data (including 0.2.txt, 0.5.txt and 0.8.txt) (Download), Sunspot (Download)

#Set 6 (Download) data (Download)

#Set 7 (Download)

#Set 8 (Download) fitinput (Download)

Marks: