Athanasios Chatzistavrakidis was awarded with NAWA's Ulam Fellowship and will join us for six month

Computational Methods I

USOS

A visualization of the  Mandelbrot set This is the mandatory Computational Methods I course of the Master in Theoretical Physics at the University of Wrocław. It is tailored towards master and PhD students who are familiar with

  • computers and their operating systems (Linux is preferred, but Windows or MacOS will work, too)
  • at least one programming language, ideally Python which we will use in the course
  • classical and quantum mechanics.

There are many good books on the subject, four that I like are

  • Landau , Páez, Bordeianu: Computational Physics: Problem Solving with Python
  • Scherer: Computational Physics - Simulation of Classical and Quantum Systems
  • Matthes: Python Crash Course - A Hands-On, Project-Based Introduction to Programming
  • Johansson: Numerical Python

We will follow mostly the first one in the lectures. There will be 2 hours of lectures and 2 hours of labs each week. Exercises will be posted here a week before the tutorial they are discussed in. Please keep in mind that active participation in the labs and especially submitting the solution to the posed problems is important to pass the course. M.Sc. Biplab Mahato will be the assistant for this course. Please fell free to contact him or me if you have any questions. For information about credits points, please refer to the syllabus or contact me directly.

Location: There have been some changes of lecture room. We are now in the computer lab 120 for both, the lectures and the labs.

Recordings/hybrid participation: The lectures will be streamed on YouTube with the link for each of them given below. This should help if you are not able to physically attend.

Exam: Details are still to be fixed.

Development environment: In this course we will use the programming language Python with a notebook interface, called Jupyter notebooks. To get all this running, and avoid possible problems with incompatible versions of Python packages, we will use a standardized development environment. To set it up, please first install Visual Studio Code. On Linux, you will most likely have it already in your package manager. Alternatively, you can also download it here. Next, you need to clone the git repository for this course. To this end, open VS Code and click on the two documents (Explorer, Ctrl+Shift+E) to find something which looks like this

Clone a repo in VS code

Now, click on "Clone Repository" and enter the URL of our git repository

https://www.fhassler.de/git/public/ComputationalMethodsI

like this

Enter the repo's URL

Finally you have to install all packages for the virtual environment, we use to run our Python code and notebooks. In the main menu, click on Terminal and then Run Build Task... (Ctrl+Shift+B). After this you should see some activity in the terminal on the bottom of the screen. If there are no errors, you have successfully installed the virtual environment. Now you are ready to add your own notebooks or run some of the already provided.

Additional material for the individual lectures, including the exercises which we discuss in the labs, is given below:

  1. A quick introduction to Python
    Lecture04.10.2024 06:15, notes, notebook, show recording
    Tutorial04.10.2024 08:15, exercise
  2. Different representations of numbers and their numerical errors
    Lecture11.10.2024 06:30, notes, notebook, show recording
    Tutorial11.10.2024 08:15, exercise
  3. Numerical differentiation and integration
    Lecture18.10.2024 06:30, show recording
  4. Ordinary differential equations
    Lecture25.10.2024 06:30, show recording
  5. Classical dynamics with regular and chaotic behavior
    Lecture01.11.2024 07:30
  6. Methods to solve non-linear equations
    Lecture08.11.2024 07:30
  7. Iteration, bifurcation, self-similarity and chaos
    Lecture22.11.2024 07:30
  8. Linear algebra, in particular finding eigenvalues and eigenvectors
    Lecture29.11.2024 07:30
  9. Quantum dynamics, tunneling in a double-well potential
    Lecture06.12.2024 07:30
  10. Generating random numbers
    Lecture13.12.2024 09:15
  11. Brownian motion and the random walk
    Lecture20.12.2024 07:30
  12. Monte-Carlo simulations
    Lecture10.01.2025 07:00
  13. The two dimensional Ising model
    Lecture17.01.2025 07:00
  14. Partial differential equations
    Lecture24.01.2025 07:30
  15. Neural networks
    Lecture31.01.2025 07:30
Based on course 13, last update on October 11th 2024, 10:07:27 | Build 561 on October 13th 2024, 19:30:18 | Times and dates shown in UTC+00:00 | Contact