pyMOR School and User Meeting

pyMOR School is an annual event for future and current pyMOR users and developers. The sixth iteration of pyMOR School will take place in Münster, Germany, from Monday noon, August 26 to Friday noon, August 30. Experienced users of pyMOR and related software packages are especially invited to join us for the integrated User Meeting from Thursday morning (August 29) to Friday noon (August 30). A code sprint will take place on Thursday evening.

The School will offer interactive introductory lectures on some of the most important MOR methods and how to use these methods with pyMOR. We will also discuss pyMOR's technical design, how to contribute to pyMOR and present some more advanced applications. In the interactive sessions, participants will have the opportunity to either get more hands-on experience with pyMOR through exercise problems or to work on integrating pyMOR into their projects with the help of the pyMOR developers.

The User Meeting will feature user-contributed talks showcasing pyMOR-related projects and discussion sessions to shape pyMOR's future development. The code sprint is an opportunity to get involved in pyMOR development with the help of the pyMOR developers. Of course, existing users and previous School participants are also invited to join us for the whole week of pyMOR School.


The pyMOR School 2024 is organized by:

  • Linus Balicki (Virginia Tech)
  • Hendrik Kleikamp (University of Münster)
  • Petar Mlinarić (Virginia Tech)
  • Stephan Rave (University of Münster)
  • Jens Saak (MPI Magdeburg)

Previous Editions

  1. Magdeburg (2019)
  2. Online (2020)
  3. Münster (2021)
  4. Magdeburg (2022)
  5. Berlin (2023)


pyMOR is a free software library for building model order reduction applications with the Python programming language. Implemented algorithms include reduced basis methods as well as system-theoretic methods. Some of the available methods are:

  • Greedy basis generation,
  • Proper Orthogonal Decomposition (POD),
  • Discrete Empirical Interpolation Method (DEIM),
  • POD-Greedy,
  • Balanced Truncation,
  • Iterative Rational Krylov Algorithm (IRKA),
  • models based on artificial neural networks,
  • Dynamic Mode Decomposition (DMD).

All algorithms in pyMOR are formulated in terms of abstract interfaces for seamless integration with external PDE solver packages. Currently, there is support for deal.II, DUNE, FEniCS, and NGSolve. Custom (domain specific) solvers can be easily integrated with pyMOR. Moreover, pure Python implementations of FEM (Finite Element Method) and FVM (Finite Volume Method) discretizations using the NumPy/SciPy scientific computing stack are provided for getting started quickly.

Model Order Reduction

The numerical simulation of mathematical models described by partial differential equations (PDEs) or large systems of ordinary differential equations (ODEs) is nowadays an important tool for research in almost every scientific discipline. Yet, the use of such models is often limited by the available computational resources.

Over the last decade, a variety of algorithms have been developed which compute, for a given numerical ODE/PDE model, a mathematically certified surrogate that can be simulated in a small fraction of the time required for the solution of the original model. These techniques, known as model order reduction (MOR), are now becoming an integral part in many simulation workflows which otherwise would be infeasible, even on the largest available supercomputers.

See the MOR Wiki for more information.