pyMOR - Model Order Reduction with Python. pyMOR is a software library developed at the University of Münster for building model order reduction applications with the Python programming language. Its main focus lies on the application of reduced basis methods to parameterized partial differential equations. All algorithms in pyMOR are formulated in terms of abstract interfaces for seamless integration with external high-dimensional PDE solvers. Moreover, pure Python implementations of finite element and finite volume discretizations using the NumPy/SciPy scientific computing stack are provided for getting started quickly.

References in zbMATH (referenced in 12 articles , 1 standard article )

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  1. Gunzburger, M.; Iliescu, T.; Mohebujjaman, M.; Schneier, M.: An evolve-filter-relax stabilized reduced order stochastic collocation method for the time-dependent Navier-Stokes equations (2019)
  2. Lehrenfeld, Christoph; Rave, Stephan: Mass conservative reduced order modeling of a free boundary osmotic cell swelling problem (2019)
  3. Buhr, Andreas; Smetana, Kathrin: Randomized local model order reduction (2018)
  4. Himpe, Christian; Leibner, Tobias; Rave, Stephan: Hierarchical approximate proper orthogonal decomposition (2018)
  5. Buhr, Andreas; Engwer, Christian; Ohlberger, Mario; Rave, Stephan: ArbiLoMod, a simulation technique designed for arbitrary local modifications (2017)
  6. Buhr, Andreas; Engwer, Christian; Ohlberger, Mario; Rave, Stephan: ArbiLoMod: local solution spaces by random training in electrodynamics (2017)
  7. Ohlberger, Mario; Rave, Stephan: Localized reduced basis approximation of a nonlinear finite volume battery model with resolved electrode geometry (2017)
  8. Milk, René; Rave, Stephan; Schindler, Felix: PyMOR -- generic algorithms and interfaces for model order reduction (2016)
  9. Ohlberger, Mario; Rave, Stephan; Schindler, Felix: Model reduction for multiscale lithium-ion battery simulation (2016)
  10. Quarteroni, Alfio; Manzoni, Andrea; Negri, Federico: Reduced basis methods for partial differential equations. An introduction (2016)
  11. Tobias Leibner, Rene Milk, Felix Schindler: Extending DUNE: The dune-xt modules (2016) arXiv
  12. Ohlberger, M.; Schindler, F.: Error control for the localized reduced basis multiscale method with adaptive on-line enrichment (2015)