2020_online_course

Materials for the 2020 pyMOR online course

Installing pyMOR

Since we are using a feature branch for the exercise and tutorial materials you will need to install pyMOR following the following instructions should you want to work on the exercises on your own machine.

Conda (OSX, Windows, Linux)

Manylinux Wheels (Linux)

Literature

Python programming

Note that due to time constraints, we are unable to give an introduction to Python/NumPy/SciPy, but there are plenty of free online resources to learn the basics:

• for inexperienced programmers:
  • https://greenteapress.com/wp/think-python-2e/
  • https://automatetheboringstuff.com/
  • https://link.springer.com/book/10.1007/978-3-319-32428-9
  • https://python.swaroopch.com/
  • https://product.hubspot.com/blog/git-and-github-tutorial-for-beginners
• for more experienced programmers:
  • https://docs.python.org/3/tutorial/index.html
  • https://diveintopython3.problemsolving.io/
  • https://docs.python-guide.org/
  • https://docs.scipy.org/doc/numpy/user/quickstart.html
  • https://docs.scipy.org/doc/numpy/user/numpy-for-matlab-users.html
  • http://hyperpolyglot.org/numerical-analysis
  • https://matplotlib.org/tutorials/index.html

Model Order Reduction

There are several monographs available on model order reduction. In particular:

• P. Benner, M. Ohlberger, A. Cohen, K. Willcox, "Model Reduction and Approximation: Theory and Algorithms", 2017
• A. Quarteroni, A. Manzoni, F. Negri, "Reduced Basis Methods for Partial Differential Equations", 2016
• J. S. Hesthaven, G. Rozza, B. Stamm, "Certified Reduced Basis Methods for Parametrized Partial Differential Equations", 2016
• A. C. Antoulas, "Approximation of Large-Scale Dynamical Systems", 2005

Freely available lecture notes are:

• Uni Stuttgart RB lecture notes
• Uni Hamburg MOR lecture notes

Also see:

• MOR Wiki