Advanced course on Computational Physics and Optimization for Ph.D. and MS students (Spring-Summer 2026)
This course is devoted to advanced and more recent topics in computational methods for physics and including some topics for Optimization.
Link for class http://meet.google.com/vfc-mtiw-osd
Link for my previous lectures on Selected Topics
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)
- Preliminary Part A (Download)
- Preliminary Part B (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)
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Hartmann, Alexander K., and Heiko Rieger. Optimization algorithms in physics. Vol. 2. Berlin: Wiley-Vch, 2002.
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Hartmann, Alexander K., and Heiko Rieger, eds. "New optimization algorithms in physics." (2004): 134411.
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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)
My previous lectures (Link), (Link), (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)
- Some examples for numerical analysis by Mathematica By Adeela Afzal (Download) & Video 1 (Link) & Video 2 (Link)
- A sample for plotting figure by Mathematica with its export commands (Download)
My lectures on the Board
Lecture 1 (Download) uploaded elsewhere
Lecture 2 (Download) uploaded elsewhere
Lecture 3 (Download)
Lecture 4 (Download)
Lecture 5 (Download) (Download), (Download), (Download), (comp981220)
Lecture 6 (Download) (Download), (Download)
Exams timeline
First Midterm (80 points)
| St. number | midterm1-oral-25 | midterm1-writing(55) | first_midterm(80) |
| 403416091 | 12.5 | 46 | 58.5 |
| 403416093 | 25 | 50 | 75 |
| 403416088 | 25 | 53 | 78 |
| 403416084 | 20 | 50 | 70 |
| 403416085 | 20 | 43 | 63 |
| 403416096 | 20 | 50.5 | 70.5 |
| 403416092 | 7.5 | 31.5 | 39 |
Exercises:
# Set 1
