pyMOR - Model Order Reduction with Python
We are proud to announce the release of pyMOR 2023.2! This release features new and improved tutorials and new Operators which enable fast computation for certain structured problems.
Over 375 single commits have entered this release. For a full list of changes see here.
We are proud to announce the release of pyMOR 2023.1! pyMOR now comes with three new MOR methods for port-Hamiltonian systems, a new data-driven MOR method, optimization methods for parametric problems, and an improved experience for Jupyter users.
Over 880 single commits have entered this release. For a full list of changes see here.
pyMOR 2023.1 contains contributions by @TiKeil, @steff-mueller, @MohamedAdelNaguib, @Jonas-Nicodemus, and @peoe. See here for more details.
This is a bugfix release. It includes a fix related to time-stepping of reduced-order LTIModels, a visualization fix concerning ipywidgets, and an updated "Building a Reduced Basis" tutorial due to the new RNG.
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. For a full list of changes see here.
pyMOR 2022.2 contains contributions by @TiKeil, @HenKlei, @peoe and @artpelling. We are also happy to welcome Hendrik as a new main developer! See here for more details.
This is a bugfix-only release. Since the release of 2022.2.0 version 8 of ipywidgets was released, which is incompatible with our current Jupyter notebook visualizations. As a stop-gap measure we've pinned ipywidgets<8 in our packaging.
Additionally this bugfix release removes spurious import warnings in our documentation and tutorials.
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. For a full list of changes see here.
pyMOR 2022.1 contains contributions by @pbuchfink, @mdessole, @HenKlei, @peoe, @artpelling and @ullmannsven. See here for more details.
This is a bugfix release. The only change compared to 2021.2.0 is preventing users to face https://github.com/pymor/pymor/issues/1533 due to an upstream bug in qtpy
The full release notes for 2021.2 can be found here.
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.
Over 300 single commits have entered this release. For a full list of changes see here.
pyMOR 2021.2 contains contributions by Tim Keil, Jonas Nicodemus and Henrike von Hülsen. See here for more details.
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 see here.
pyMOR 2021.1 contains contributions by Tim Keil, Hendrik Kleikamp, Josefine Zeller and Meret Behrens.
Read the release notes for more details.
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:
You can read the full release notes at https://docs.pymor.org/2020.2.0/release_notes/all.html