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pyMOR - Model Order Reduction with Python
pyMOR is a software library for building model order reduction applications with the Python programming language. All algorithms in pyMOR are formulated in terms of abstract interfaces, allowing generic implementations to work with different backends, from NumPy/SciPy to external partial differential equation solver packages.
- Reduced basis methods for parametric linear and non-linear problems.
- System-theoretic methods for linear time-invariant systems.
- Neural network-based methods for parametric problems.
- Proper orthogonal decomposition.
- Dynamic mode decomposition.
- Rational interpolation of data (Loewner, AAA).
- Numerical linear algebra (Gram-Schmidt, time-stepping, ...).
- Pure Python implementations of finite element and finite volume discretizations using the NumPy/SciPy scientific computing stack.
pyMOR is licensed under BSD-2-clause. See LICENSE.txt.
If you use pyMOR for academic work, please consider citing our publication:
R. Milk, S. Rave, F. Schindler pyMOR - Generic Algorithms and Interfaces for Model Order Reduction SIAM J. Sci. Comput., 38(5), pp. S194--S216, 2016
Installation via pip
We recommend installation of pyMOR in a virtual environment.
pyMOR can easily be installed with the pip command. Please note that pip versions prior to 21.1 might have problems resolving all dependencies, so running the following first is recommended:
pip install --upgrade pip
If you are not operating in a virtual environment, you can pass the optional
--user argument to pip.
pyMOR will then only be installed for your local user, not requiring
Latest Release (without Optional Dependencies)
For an installation with minimal dependencies, run
pip install pymor
Note that most included demo scripts additionally require Qt bindings such as
pyside2 to function.
Therefore we recommend install pyMOR with the
pip install pymor[gui] # 2023.1 and later
Latest Release (with all Optional Dependencies)
The following installs the latest release of pyMOR on your system with most optional dependencies:
pip install pymor[full]
There are some optional packages not included with
because they need additional setup on your system:
mpi4py: support of MPI distributed models and parallelization of greedy algorithms (requires MPI development headers and a C compiler):
pip install mpi4py
Slycot: dense matrix equation solvers for system-theoretic methods and H-infinity norm calculation (requires OpenBLAS headers and a Fortran compiler):
pip install slycot
pip install pymess
Latest Development Version
To install the latest development version of pyMOR, execute
pip install 'pymor[full] @ git+https://github.com/pymor/pymor'
which requires that the git version control system is installed on your system.
Current Release Branch Version
From time to time, the main branch of pyMOR undergoes major changes and things might break (this is usually announced in our discussion forum), so you might prefer to install pyMOR from the current release branch:
pip install 'pymor[full] @ git+https://email@example.com'
Release branches will always stay stable and will only receive bugfix commits after the corresponding release has been made.
Installation via conda/mamba
We recommend installation of pyMOR in a conda environment with mamba.
pyMOR can be installed using conda/mamba by running
conda install -c conda-forge mamba mamba install -c conda-forge pymor
To build the documentation locally, run the following from inside the root directory of the pyMOR source tree:
This will generate HTML documentation in
External PDE Solvers
pyMOR has been designed with easy integration of external PDE solvers in mind.
We provide bindings for the following solver libraries:
MPI-compatible wrapper classes for dolfin linear algebra data structures are shipped with pyMOR (
pymor.bindings.fenics). For an example see
pymordemos.thermalblock_simple. It is tested using FEniCS version 2019.1.0.
Python bindings and pyMOR wrapper classes can be found here.
Wrapper classes for the NGSolve finite element library are shipped with pyMOR (
pymor.bindings.ngsolve). For an example see
pymordemos.thermalblock_simple. It is tested using NGSolve version v6.2.2104.
A simple example for direct integration of pyMOR with a a custom solver
can be found in
An alternative approach is to import system matrices from file and use
Environments for pyMOR Development and Tests
Please see the Developer Documentation.
Should you have any questions regarding pyMOR or wish to contribute, do not hesitate to send us an email at
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