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pyMOR - Model Order Reduction with Python

Goal: pyMOR is a software library for building model order reduction applications with the Python programming language. Implemented algorithms include reduced basis methods for parametric linear and non-linear problems, as well as system-theoretic methods such as balanced truncation or IRKA (Iterative Rational Krylov Algorithm). All algorithms in pyMOR are formulated in terms of abstract interfaces for seamless integration with external PDE (Partial Differential Equation) solver packages. 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.

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Project log

Hendrik Kleikamp
added an update
We are proud to announce the release of pyMOR 2022.2!
pyMOR now comes with three new data-driven MOR methods and time domain analysis for linear time-invariant systems.
Over 500 single commits have entered this release.
pyMOR 2022.2 contains contributions by Tim Keil, Hendrik Kleikamp, Peter Oehme and Art Pelling. We are also happy to welcome Hendrik as a new main developer!
 
René Fritze
added an update
We are proud to announce the release of pyMOR 2022.1!
pyMOR now comes with support for discrete-time systems
and structure-preserving MOR for symplectic systems.
The neural network based reductors gained many new features,
while the VectorArray implementation got simplified.
We have added an experimental FEniCS discretizer
and extended functionality for randomized linear algebra.
Over 760 single commits have entered this release.
pyMOR 2022.1 contains contributions by Patric Buchfink, Monica Dessole,
Hendrik Kleikamp, Peter Oehme, Art Pelling and Sven Ullmann.
 
René Fritze
added an update
pyMOR 2021.2 (December 22, 2021)
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We are proud to announce the release of pyMOR 2021.2! New features in
this release are the addition of Dynamic Mode Decomposition for data-driven
model order reduction and the formalization of model inputs. Further,
general output error bounds for Reduced Basis reductors and experimental
scikit-fem support as an alternative to the builtin discretizers were
added. Wachspress' shifts accelerate the solution of Lyapunov equations
for symmetric system matrices.
 
René Fritze
added an update
We are proud to announce the release of pyMOR 2021.1.0! This release includes
several new reductors for LTI systems. In particular, methods for reducing and
analyzing unstable systems have been added. ANNs can now be used in order to
directly approximate output quantities. Furthermore, it is now possible to
work with time-dependent parameters in pyMOR.
Over 700 single commits have entered this release. For a full list of changes
pyMOR 2021.1 contains contributions by Tim Keil, Hendrik Kleikamp, Josefine Zeller
and Meret Behrens.
Read the release notes for more details:
 
René Fritze
added an update
We are very happy to invite you to the third pyMOR School, which will
take place from 4-8 October 2021
at the Mathematics Münster Cluster of Excellence Germany.
This school is targeted at anyone interested to apply model order
reduction in their work using pyMOR (https://pymor.org). The school
will consist of both introductory lectures to model order reduction
and pyMOR as well as programming sessions. Participants are encouraged
to bring their own problems to work on. Basic knowledge of Python and
packages from the SciPy stack (NumPy, SciPy, matplotlib) is assumed.
For participants without any prior experience with Python, we have
linked introductory material on the school homepage.
You can register online before 3 September 2021. For details, see:
 
Mario Ohlberger
added a research item
In this contribution we present a survey of concepts in localized model order reduction methods for parameterized partial differential equations. The key concept of localized model order reduction is to construct local reduced spaces that have only support on part of the domain and compute a global approximation by a suitable coupling of the local spaces. In detail, we show how optimal local approximation spaces can be constructed and approximated by random sampling. An overview of possible conforming and non-conforming couplings of the local spaces is provided and corresponding localized a posteriori error estimates are derived. We introduce concepts of local basis enrichment, which includes a discussion of adaptivity. Implementational aspects of localized model reduction methods are addressed. Finally, we illustrate the presented concepts for multiscale, linear elasticity and fluid-flow problems, providing several numerical experiments. This work has been submitted as a chapter in P. Benner, S. Grivet-Talocia, A. Quarteroni, G. Rozza, W.H.A. Schilders, L.M. Sileira. Handbook on Model Order Reduction. Walter De Gruyter GmbH, Berlin, 2019+.
René Fritze
added an update
We are proud to announce the release of pyMOR 2020.2! This release extends pyMOR’s support for non-intrusive model reduction via artificial neural networks to non-stationary models. Built-in support for computing parameter sensitivities simplifies the use of pyMOR in PDE-constrained optimization applications. pyMOR’s documentation has been extended by three new tutorials, and all tutorial code can now easily be executed using binder.
Over 520 single commits have entered this release. For a full list of changes see here.
pyMOR 2020.2 contains contributions by Tim Keil and Hendrik Kleikamp.
 
René Fritze
added an update
pyMOR 2020.1.2 is a bugfix release:
- for the PyMESS bindings we now ensure solve_lyap_dense returns a NumPy array
- to avoid setup problems, and following NumPy we require setuptools < 49.2.0
- improved consistency in Newton and line search logging output
 
René Fritze
added an update
We are proud to announce the release of pyMOR 2020.1!
Highlights of this release are support for non-intrusive model order reduction using artificial neural networks,
the subspace accelerated dominant pole algorithm (SAMDP) and the implicitly restarted
Arnoldi method for eigenvalue computation.
Parameter handling in pyMOR has been simplified, and a new series of hands-on tutorials helps getting started using pyMOR more easily.
You can read the full release notes at https://docs.pymor.org/2020.1.1/release_notes.html
 
Petar Mlinarić
added 2 research items
pyMOR is a free software library for model order reduction that includes both reduced basis and system-theoretic methods. All methods are implemented in terms of abstract vector and operator interfaces, which allows direct integration of pyMOR's algorithms with a wide array of external PDE solvers. In this contribution, we give a brief overview of the available methods and experimentally compare them for the parametric instationary thermal-block benchmark defined in [S. Rave and J. Saak, Thermal Block. MORwiki - Model Order Reduction Wiki, 2020. http://modelreduction.org/index.php/Thermal_Block].
This paper shows recent developments in pyMOR, in particular the addition of system‐theoretic methods. All methods are implemented using pyMOR's abstract interfaces, which allows the application to partial differential equation (PDE) models implemented with third‐party libraries. We demonstrate this by applying balanced truncation to a PDE model discretized in FEniCS.
René Fritze
added an update
Highlights of this release are:
  • Improved model and reductor design makes pyMOR easier to extend.
  • Extended VectorArray interface with generic complex number support.
  • Improved and extended system-theoretic MOR methods.
  • Builtin support for model outputs and parameter sensitivities.
 
Stephan Rave
added an update
The pyMOR development team is happy to announce the release of version 2019.2 of pyMOR (www.pymor.org).
pyMOR is a software library for building model order reduction applications with the Python programming language. Implemented algorithms include reduced basis methods for parametric linear and non-linear problems, as well as system-theoretic methods such as balanced truncation or IRKA. All algorithms in pyMOR are formulated in terms of abstract interfaces for seamless integration with external PDE solver packages. Moreover, pure Python implementations of finite element and finite volume discretizations using the NumPy/SciPy scientific computing stack are provided for getting started quickly.
Highlights of this release are:
  • Improved model and reductor design makes pyMOR easier to extend.
  • Extended VectorArray interface with generic complex number support.
  • Improved and extended system-theoretic MOR methods.
  • Builtin support for model outputs and parameter sensitivities.
The full release notes can be found under the following address:
 
René Fritze
added a project goal
pyMOR is a software library for building model order reduction applications with the Python programming language. Implemented algorithms include reduced basis methods for parametric linear and non-linear problems, as well as system-theoretic methods such as balanced truncation or IRKA (Iterative Rational Krylov Algorithm). All algorithms in pyMOR are formulated in terms of abstract interfaces for seamless integration with external PDE (Partial Differential Equation) solver packages. 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.