Advanced course on Computational Physics and Optimization for Ph.D. and MS students  (Winter-Spring 2024)

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

Link for class https://vc15.sbu.ac.ir/class-4022161421201/

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

Link for my previous lectures on Computational Physics

Link for my lectures on Optimization (Khajeh Nasir Digital Library, 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 good movie presented by Pooyan Goodarzi to connect the server, remotely (Link)
• A good link for shell script programming (Link)
• Some of my bash samples (Download)
• A sample for Linux commands skills (Download)
• A good link for programming tutorials  (Link)
• A good presentation by Kip R. Irvine for number representation (Download)

• Hartmann, Alexander K., and Heiko Rieger. Optimization algorithms in physics. Vol. 2. Berlin: Wiley-Vch, 2002.

• Hartmann, Alexander K., and Heiko Rieger, eds. "New optimization algorithms in physics." (2004): 134411.

• Mezard, Marc, and Andrea Montanari. Information, physics, and computation. Oxford University Press, 2009.

• 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)
• 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)
• My lectures on Errors and PDF (Download) (Download)
• Some of my Python programs (Download)
• Visualization by Matlab (link)
• Discretization approaches (Download) (Download)
• My note about deterministic Fractals (Download) & (see this link)
• A good reference for errors analysis (see this link)
• A proper series for Machine learning (Part 1), (part 2)
• School and Workshop on statistical analysis of stochastic fields (Link) (Link)
• A pedagogical matter for MCMC (Link)

Some of my lectures on the Board

My previous lectures (Link) and (Link) and (Link)

• Preliminary Part (Download)
• Data Science 1 (Download)
• Number Representation (Download) (Download)
• Data Science 2 (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
• Simulation-Based-Inferences workshop by Mohammad Hossein Jalali (Download)

 

Exams timeline

First midterm will be held on 30 Farvardin 1403 at 9:00 a.m.  (Questions and Data)

Second midterm

Final exam

 

Exercises:

# Set 1 (Download)  Necessary files (Q1-Part A)  (Q1-Part B) Updated on 19/02/2024 (corresponding: Including the Traveling Salesman Problem )

# Set 2 (Download)  Necessary files (Data_new) (Datatypes.pdf)

# Set 3 (Download)  Necessary files  for Q1 (Download); for Q5-8 (Download) (data including 0.2.txt, 0.5.txt and 0.8.txt) for Q9 ((1+1)-Dimensional data (Download) & (1+2)-Dimensional data (Download) & (Download))

# Set 4 (Download) Necessary file (Download)

# Set 5 (Download) Necessary file (Download)

# Set 6 (Download)

# Set 7 (Download)