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.

Manylinux Wheels (Linux)

To install pyMOR the appropiate wheel for you Python version (supported are 3.6,3.7 and 3.8) follow these steps in a terminal bash session:

export TARGET_DIRECTORY=${HOME}/pymor_course
mkdir ${TARGET_DIRECTORY}
git clone https://github.com/pymor/2020_online_course ${TARGET_DIRECTORY}/material
python3 -m virtualenv ${TARGET_DIRECTORY}/virtualenv
. ${TARGET_DIRECTORY}/virtualenv/bin/activate
export WHL_VERSION=$(python -c 'import sys; m="m"; vi=sys.version_info; print(f"cp{vi[0]}{vi[1]}-cp{vi[0]}{vi[1]}{m if vi[1] < 8 else str()}-manylinux1_x86_64")')
pip install ${TARGET_DIRECTORY}/material/packages/wheels/linux-64/pymor-2020_online_course-${WHL_VERSION}.whl[full]

# now you can start a Jupter Notebook server to work on the exercises:
jupyter notebook --notebook-dir=${TARGET_DIRECTORY}/material/exercises

"plain" pip

You can also install directly from source, but note this will need a minimal C compiler setup:

export TARGET_DIRECTORY=${HOME}/pymor_course
mkdir ${TARGET_DIRECTORY}
git clone https://github.com/pymor/2020_online_course ${TARGET_DIRECTORY}/material
python3 -m virtualenv ${TARGET_DIRECTORY}/virtualenv
. ${TARGET_DIRECTORY}/virtualenv/bin/activate

pip install -r ${TARGET_DIRECTORY}/material/requirements.txt
# now you can start a Jupter Notebook server to work on the exercises:
jupyter notebook --notebook-dir=${TARGET_DIRECTORY}/material/exercises

Docker

If you have a working docker setup you can use also to work on the exercises.

# first build the image
docker build --build-arg NB_USER=${USER} --build-arg NB_UID=$(id -u) -t pymor_course -f .binder/Dockerfile  .
# then start the Jupyter server
docker run pymor_course:latest bash -c "jupyter notebook --ip 0.0.0.0 --no-browser --notebook-dir=/pymor --NotebookApp.disable_check_xsrf=True"
# now open the displayed URL (127.0.0.1) in your browser

myBinder.org

If cannot or do not want to install pymMOR you can play with the exercises and tutorials using the mybinder.org infrastructure:

Please note that this a free service with usage limitations (no persistent data, compute capacity, concurrent users), with which we are not affiliated. You can read more about it here

pyMOR documentation

You can find the documentation here

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