Advanced course on Selected Topics on Computational Physics and Optimization and Stochastic Fields (Winter-Spring 2024)

The virtual attending every week at Thursday (14:00-16:00) via https://meet.google.com/iph-qqda-zcz

My Lectures

Course 1 (Download)

Course 2 (Download) & (Datatypes.pdf)

Course 3 (Download)

Course 4 (Download)   Video (14030109), Video (14030116)

Course 5 (Download), Video (140301)

Course 6 (Download), (Download), (Download) Video (14030130)

Also see

(stochastic991212), (stochastic991217), (stochastic991219), (stochastic991224), (stochastic991226), (level CS),  (Level Laser) and (Peak Planck), (stochastic000122), (stochastic000124), (stochastic000129), (stochastic000131)

Course 7 (Download), Video (14030206)

Large scale Galaxy Bias (Link)

Bias Factor in anisotropic stochastic fields (Link)

On the spatial correlations of Abell Clusters (Link)

A paper by Murad S. Taqqu  for mathematics of fields (Link)

(stochastic000129)

• 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)

 

 

Exercises:

# Set 1 (Download)  Necessary files (Q2-Part A)  (Q2-Part B) (Q2-Part E) (Data_new)

# Set 2 (Download)  Necessary files (DataSet2)

# Set 3 (Download) data (including 0.2.txt, 0.5.txt and 0.8.txt) (Download)

# Set 4 (Download)

# Set 5 (Download) Necessary file (Data)

# Set 6 (Download)