PyMOR - Revision history

Saak at 11:06, 12 January 2026

2026-01-12T11:06:49Z

← Older revision		Revision as of 11:06, 12 January 2026
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	* Model order reduction using artificial neural networks.		* Model order reduction using artificial neural networks.
	* Data-driven model order reduction with Dynamic Mode Decomposition, Loewner matrices, Eigensystem Realization, and parametric AAA.		* Data-driven model order reduction with Dynamic Mode Decomposition, Loewner matrices, Eigensystem Realization, and parametric AAA.
			* Data-driven model order reduction using machine learning methods such as artificial neural networks or the Vectorial Kernel Orthogonal Greedy Algorithm (VKOGA).
	* [[:Wikipedia:Gram_schmidt\|Gram-Schmidt algorithm]] supporting re-orthogonalization for improved numerical accuracy.		* [[:Wikipedia:Gram_schmidt\|Gram-Schmidt algorithm]] supporting re-orthogonalization for improved numerical accuracy.
	* Time-stepping and Newton algorithms, as well as generic iterative linear solvers.		* Time-stepping and Newton algorithms, as well as generic iterative linear solvers.
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	* [https://dune-project.org/ DUNE]		* [https://dune-project.org/ DUNE]
	* [https://fenicsproject.org/ FEniCS]		* [https://fenicsproject.org/ FEniCS]
	* [https://fenicsproject.org/ FEniCSx] ~~(in preparation, see feature branch)~~		* [https://fenicsproject.org/ FEniCSx]
	* [https://sourceforge.net/projects/ngsolve/ NGSolve]		* [https://sourceforge.net/projects/ngsolve/ NGSolve]
	* [https://scikit-fem.readthedocs.io/ scikit-fem] (experimental)		* [https://scikit-fem.readthedocs.io/ scikit-fem] (experimental)

Saak at 09:26, 14 September 2024

2024-09-14T09:26:08Z

← Older revision		Revision as of 09:26, 14 September 2024
Line 30:		Line 30:
	* [https://dune-project.org/ DUNE]		* [https://dune-project.org/ DUNE]
	* [https://fenicsproject.org/ FEniCS]		* [https://fenicsproject.org/ FEniCS]
			* [https://fenicsproject.org/ FEniCSx] (in preparation, see feature branch)
	* [https://sourceforge.net/projects/ngsolve/ NGSolve]		* [https://sourceforge.net/projects/ngsolve/ NGSolve]
	* [https://scikit-fem.readthedocs.io/ scikit-fem] (experimental)		* [https://scikit-fem.readthedocs.io/ scikit-fem] (experimental)

Mlinaric: Update for pyMOR 2023.1

2023-07-07T00:14:59Z

Update for pyMOR 2023.1

Lund: remove link to list of abbrev

2023-05-11T12:22:57Z

remove link to list of abbrev

Saak at 17:09, 30 December 2022

2022-12-30T17:09:07Z

← Older revision		Revision as of 17:09, 30 December 2022
Line 18:		Line 18:
	* Balanced-based and interpolation-based reduction methods for first-order and second-order linear time-invariant systems.		* Balanced-based and interpolation-based reduction methods for first-order and second-order linear time-invariant systems.
	* Model order reduction using artificial neural networks.		* Model order reduction using artificial neural networks.
	* Data-driven model order reduction with Dynamic Mode Decomposition		* Data-driven model order reduction with Dynamic Mode Decomposition, Eigensystem Realization, parametric AAA.
	* [[:Wikipedia:Gram_schmidt\|Gram-Schmidt algorithm]] supporting re-orthogonalization for improved numerical accuracy.		* [[:Wikipedia:Gram_schmidt\|Gram-Schmidt algorithm]] supporting re-orthogonalization for improved numerical accuracy.
	* Time-stepping and Newton algorithms, as well as generic iterative linear solvers.		* Time-stepping and Newton algorithms, as well as generic iterative linear solvers.

Mlinaric: Improvements and update on pyMOR

2022-07-21T12:27:15Z

Improvements and update on pyMOR

Saak at 10:12, 23 December 2021

2021-12-23T10:12:05Z

← Older revision		Revision as of 10:12, 23 December 2021
Line 18:		Line 18:
	* Balanced-based and interpolation-based reduction methods for first-order and second-order linear time-invariant systems.		* Balanced-based and interpolation-based reduction methods for first-order and second-order linear time-invariant systems.
	* Model order reduction using artificial neural networks.		* Model order reduction using artificial neural networks.
			* Data-driven model order reduction with Dynamic Mode Decomposition
	* [[:Wikipedia:Gram_schmidt\|Gram-Schmidt algorithm]] supporting re-orthogonalization for improved numerical accuracy.		* [[:Wikipedia:Gram_schmidt\|Gram-Schmidt algorithm]] supporting re-orthogonalization for improved numerical accuracy.
	* Time-stepping and Newton algorithms, as well as generic iterative linear solvers.		* Time-stepping and Newton algorithms, as well as generic iterative linear solvers.
Line 31:		Line 32:
	* [http://fenicsproject.org/ FEniCS]		* [http://fenicsproject.org/ FEniCS]
	* [http://sourceforge.net/projects/ngsolve/ NGSolve]		* [http://sourceforge.net/projects/ngsolve/ NGSolve]
			* [https://scikit-fem.readthedocs.io/ scikit-fem] (experimental)

	Pure Python implementations of discretizations using the [https://www.scipy.org NumPy/SciPy] scientific computing stack are implemented to provide an easy to use sandbox for experimentation with new model reduction approaches. '''pyMOR''' offers:		Pure Python implementations of discretizations using the [https://www.scipy.org NumPy/SciPy] scientific computing stack are implemented to provide an easy to use sandbox for experimentation with new model reduction approaches. '''pyMOR''' offers:

Saak at 15:06, 24 September 2021

2021-09-24T15:06:26Z

← Older revision		Revision as of 15:06, 24 September 2021
Line 22:		Line 22:
	* Low-rank alternating direction implicit (LR ADI) method for large-scale Lyapunov equations and bindings for matric equations solvers in [http://slicot.org SLICOT] (via [https://github.com/python-control/Slycot slycot]) and [https://www.mpi-magdeburg.mpg.de/projects/mess Py-M.E.S.S].		* Low-rank alternating direction implicit (LR ADI) method for large-scale Lyapunov equations and bindings for matric equations solvers in [http://slicot.org SLICOT] (via [https://github.com/python-control/Slycot slycot]) and [https://www.mpi-magdeburg.mpg.de/projects/mess Py-M.E.S.S].
	* Eigenvalue/pole computation using the implicitly restarted Arnoldi method and the subspace accelerated dominant pole (SAMDP) algorithm.		* Eigenvalue/pole computation using the implicitly restarted Arnoldi method and the subspace accelerated dominant pole (SAMDP) algorithm.
			* Modal truncation for kinear time-invariant systems.
			* time-dependent parameters.

	All these algorithms are formulated in terms of abstract interfaces for seamless integration with external high-dimensional [[List_of_abbreviations#PDE\|PDE]] solvers.		All these algorithms are formulated in terms of abstract interfaces for seamless integration with external high-dimensional [[List_of_abbreviations#PDE\|PDE]] solvers.

Mlinaric: Add to Python category

2021-07-03T18:16:52Z

Add to Python category

← Older revision		Revision as of 18:16, 3 July 2021
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	[[Category:Software]]		[[Category:Software]]
			[[Category:Python]]

	== Synopsis ==		== Synopsis ==

Mlinaric: Update and edit links

2020-12-11T17:06:36Z

Update and edit links

← Older revision		Revision as of 17:06, 11 December 2020
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	== Links ==		== Links ==
	* Official ~~website~~ https://pymor.org		* Official [https://pymor.org website],
	* Development of '''pyMOR''' can be tracked on [https://github.com/pymor/pymor GitHub],		* Development of '''pyMOR''' can be tracked on [https://github.com/pymor/pymor GitHub],
	* Online [https://docs.pymor.org documentation]		* Online [https://docs.pymor.org documentation].

	== Contact ==		== Contact ==
	For assistance with, and contributions to '''pyMOR''', the developers can be contacted via [~~http~~://~~listserv~~.~~uni-muenster.de~~/~~mailman~~/~~listinfo~~/~~pymor-dev pymor-dev@listserv.uni-muenster~~.~~de]~~		For assistance with, and contributions to '''pyMOR''', the developers can be contacted via [https://github.com/pymor/pymor/discussions GitHub discussions].

← Older revision		Revision as of 00:14, 7 July 2023
Line 5:		Line 5:

	[https://pymor.org pyMOR] is a [https://opensource.org/licenses/BSD-2-Clause BSD-licensed] software library for building model order reduction applications in the [[:Wikipedia:Python_(programming_language)\|Python programming language]].		[https://pymor.org pyMOR] is a [https://opensource.org/licenses/BSD-2-Clause BSD-licensed] software library for building model order reduction applications in the [[:Wikipedia:Python_(programming_language)\|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 and iterative rational Krylov algorithm.~~		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.
	'''pyMOR''' ~~is designed~~ from ~~the ground up for easy integration with~~ external ~~PDE~~ solver packages ~~but also offers Python-based discretizations for getting started easily~~.

	== Features ==		== Features ==
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	* Parallel adaptive greedy and POD algorithms for reduced space construction.		* Parallel adaptive greedy and POD algorithms for reduced space construction.
	* Empirical-Interpolation-Greedy and DEIM algorithms for generation of interpolation data for empirical operator interpolation.		* Empirical-Interpolation-Greedy and DEIM algorithms for generation of interpolation data for empirical operator interpolation.
	* Balanced-based and interpolation-based reduction methods for first-order ~~and~~ second-order linear time-invariant systems.		* Balanced-based and interpolation-based reduction methods for first-order, second-order, and port-Hamiltonian linear time-invariant systems.
	* Model order reduction using artificial neural networks.		* Model order reduction using artificial neural networks.
	* Data-driven model order reduction with Dynamic Mode Decomposition, Eigensystem Realization, parametric AAA.		* Data-driven model order reduction with Dynamic Mode Decomposition, Loewner matrices, Eigensystem Realization, and parametric AAA.
	* [[:Wikipedia:Gram_schmidt\|Gram-Schmidt algorithm]] supporting re-orthogonalization for improved numerical accuracy.		* [[:Wikipedia:Gram_schmidt\|Gram-Schmidt algorithm]] supporting re-orthogonalization for improved numerical accuracy.
	* Time-stepping and Newton algorithms, as well as generic iterative linear solvers.		* Time-stepping and Newton algorithms, as well as generic iterative linear solvers.
	* Low-rank alternating direction implicit (LR ADI) method for large-scale Lyapunov equations and bindings for matric equations solvers in [~~http~~://slicot.org SLICOT] (via [https://github.com/python-control/Slycot slycot]) and [https://www.mpi-magdeburg.mpg.de/projects/mess Py-M.E.S.S].		* Low-rank alternating direction implicit (LR ADI) method for large-scale Lyapunov equations and bindings for matric equations solvers in [https://www.slicot.org SLICOT] (via [https://github.com/python-control/Slycot slycot]) and [https://www.mpi-magdeburg.mpg.de/projects/mess Py-M.E.S.S].
	* Eigenvalue/pole computation using the implicitly restarted Arnoldi method and the subspace accelerated dominant pole (SAMDP) algorithm.		* Eigenvalue/pole computation using the implicitly restarted Arnoldi method and the subspace accelerated dominant pole (SAMDP) algorithm.
	* Modal truncation for linear time-invariant systems.		* Modal truncation for linear time-invariant systems.
Line 28:		Line 27:
	All these algorithms are formulated in terms of abstract interfaces for seamless integration with external high-dimensional PDE solvers.		All these algorithms are formulated in terms of abstract interfaces for seamless integration with external high-dimensional PDE solvers.
	Bindings for the following PDE solver libraries are available:		Bindings for the following PDE solver libraries are available:
	* [~~http~~://www.dealii.org/ deal.II]		* [https://www.dealii.org/ deal.II]
	* [~~http~~://dune-project.org/ DUNE]		* [https://dune-project.org/ DUNE]
	* [~~http~~://fenicsproject.org/ FEniCS]		* [https://fenicsproject.org/ FEniCS]
	* [~~http~~://sourceforge.net/projects/ngsolve/ NGSolve]		* [https://sourceforge.net/projects/ngsolve/ NGSolve]
	* [https://scikit-fem.readthedocs.io/ scikit-fem] (experimental)		* [https://scikit-fem.readthedocs.io/ scikit-fem] (experimental)

	Pure Python implementations of discretizations using the [https://~~www~~.scipy.org ~~NumPy/~~SciPy] scientific computing stack are implemented to provide an easy-to-use sandbox for experimentation with new model reduction approaches. '''pyMOR''' offers:		Pure Python implementations of discretizations using the [https://numpy.org NumPy]/[https://scipy.org SciPy] scientific computing stack are implemented to provide an easy-to-use sandbox for experimentation with new model reduction approaches. '''pyMOR''' offers:
	* Structured 1D and 2D grids, as well as an experimental Gmsh-based grid, implementing the same abstract grid interface.		* Structured 1D and 2D grids, as well as an experimental Gmsh-based grid, implementing the same abstract grid interface.
	* [[:Wikipedia:Finite_element\|Finite element]] and [[:Wikipedia:Finite_volume\|finite volume]] operators based on this interface.		* [[:Wikipedia:Finite_element\|Finite element]] and [[:Wikipedia:Finite_volume\|finite volume]] operators based on this interface.
	* SciPy/[http://crd-legacy.lbl.gov/~xiaoye/SuperLU SuperLU] based iterative and direct solvers for sparse systems.		* SciPy/[http://crd-legacy.lbl.gov/~xiaoye/SuperLU SuperLU] based iterative and direct solvers for sparse systems.
	* [[:Wikipedia:Opengl\|OpenGL]] ~~and~~ [~~http~~://matplotlib.org matplotlib] based visualizations of solutions.		* [[:Wikipedia:Opengl\|OpenGL]], [https://matplotlib.org matplotlib], and [https://github.com/K3D-tools/K3D-jupyter k3d] based visualizations of solutions.

	== References ==		== References ==
	* M. Ohlberger, S. Rave, S. Schmidt, S. Zhang. "[~~http~~://~~dx.~~doi.org/10.1007/978-3-319-05591-6_69 A Model Reduction Framework for Efficient Simulation of Li-Ion Batteries]". Springer Proceedings in Mathematics & Statistics Vol. 78: Finite Volumes for Complex Applications VII-Elliptic, Parabolic and Hyperbolic Problems, Berlin, June 2014.		* M. Ohlberger, S. Rave, S. Schmidt, S. Zhang. "[https://doi.org/10.1007/978-3-319-05591-6_69 A Model Reduction Framework for Efficient Simulation of Li-Ion Batteries]". Springer Proceedings in Mathematics & Statistics Vol. 78: Finite Volumes for Complex Applications VII-Elliptic, Parabolic and Hyperbolic Problems, Berlin, June 2014.
	* R. Milk, S. Rave, F. Schindler. "[https://doi.org/10.1137/15M1026614 pyMOR - Generic Algorithms and Interfaces for Model Order Reduction]". SIAM Journal on Scientific Computing 38(5): S194--S216, 2016.		* R. Milk, S. Rave, F. Schindler. "[https://doi.org/10.1137/15M1026614 pyMOR - Generic Algorithms and Interfaces for Model Order Reduction]". SIAM Journal on Scientific Computing 38(5): S194--S216, 2016.

← Older revision		Revision as of 12:22, 11 May 2023
Line 6:		Line 6:
	[https://pymor.org pyMOR] is a [https://opensource.org/licenses/BSD-2-Clause BSD-licensed] software library for building model order reduction applications in the [[:Wikipedia:Python_(programming_language)\|Python programming language]].		[https://pymor.org pyMOR] is a [https://opensource.org/licenses/BSD-2-Clause BSD-licensed] software library for building model order reduction applications in the [[:Wikipedia:Python_(programming_language)\|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 and iterative rational Krylov algorithm.		Implemented algorithms include reduced basis methods for parametric linear and non-linear problems, as well as system-theoretic methods such as balanced truncation and iterative rational Krylov algorithm.
	'''pyMOR''' is designed from the ground up for easy integration with external ~~[[List_of_abbreviations#~~PDE~~\|PDE]]~~ solver packages but also offers Python-based discretizations for getting started easily.		'''pyMOR''' is designed from the ground up for easy integration with external PDE solver packages but also offers Python-based discretizations for getting started easily.

	== Features ==		== Features ==
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	* Efficient error estimation for linear affinely decomposed problems.		* Efficient error estimation for linear affinely decomposed problems.
	* Empirical interpolation of arbitrary operators (with efficient evaluation of projected interpolated operators if the operator supports restriction to selected degrees of freedom).		* Empirical interpolation of arbitrary operators (with efficient evaluation of projected interpolated operators if the operator supports restriction to selected degrees of freedom).
	* Parallel adaptive greedy and ~~[[List_of_abbreviations#~~POD~~\|POD]]~~ algorithms for reduced space construction.		* Parallel adaptive greedy and POD algorithms for reduced space construction.
	* Empirical-Interpolation-Greedy and ~~[[List_of_abbreviations#DEIM\|~~DEIM]] algorithms for generation of interpolation data for empirical operator interpolation.		* Empirical-Interpolation-Greedy and DEIM algorithms for generation of interpolation data for empirical operator interpolation.
	* Balanced-based and interpolation-based reduction methods for first-order and second-order linear time-invariant systems.		* Balanced-based and interpolation-based reduction methods for first-order and second-order linear time-invariant systems.
	* Model order reduction using artificial neural networks.		* Model order reduction using artificial neural networks.
Line 26:		Line 26:
	* Time-dependent parameters.		* Time-dependent parameters.

	All these algorithms are formulated in terms of abstract interfaces for seamless integration with external high-dimensional ~~[[List_of_abbreviations#~~PDE~~\|PDE]]~~ solvers.		All these algorithms are formulated in terms of abstract interfaces for seamless integration with external high-dimensional PDE solvers.
	Bindings for the following PDE solver libraries are available:		Bindings for the following PDE solver libraries are available:
	* [http://www.dealii.org/ deal.II]		* [http://www.dealii.org/ deal.II]

← Older revision		Revision as of 12:27, 21 July 2022
Line 23:		Line 23:
	* Low-rank alternating direction implicit (LR ADI) method for large-scale Lyapunov equations and bindings for matric equations solvers in [http://slicot.org SLICOT] (via [https://github.com/python-control/Slycot slycot]) and [https://www.mpi-magdeburg.mpg.de/projects/mess Py-M.E.S.S].		* Low-rank alternating direction implicit (LR ADI) method for large-scale Lyapunov equations and bindings for matric equations solvers in [http://slicot.org SLICOT] (via [https://github.com/python-control/Slycot slycot]) and [https://www.mpi-magdeburg.mpg.de/projects/mess Py-M.E.S.S].
	* Eigenvalue/pole computation using the implicitly restarted Arnoldi method and the subspace accelerated dominant pole (SAMDP) algorithm.		* Eigenvalue/pole computation using the implicitly restarted Arnoldi method and the subspace accelerated dominant pole (SAMDP) algorithm.
	* Modal truncation for ~~kinear~~ time-invariant systems.		* Modal truncation for linear time-invariant systems.
	* ~~time~~-dependent parameters.		* Time-dependent parameters.

	All these algorithms are formulated in terms of abstract interfaces for seamless integration with external high-dimensional [[List_of_abbreviations#PDE\|PDE]] solvers.		All these algorithms are formulated in terms of abstract interfaces for seamless integration with external high-dimensional [[List_of_abbreviations#PDE\|PDE]] solvers.
Line 34:		Line 34:
	* [https://scikit-fem.readthedocs.io/ scikit-fem] (experimental)		* [https://scikit-fem.readthedocs.io/ scikit-fem] (experimental)

	Pure Python implementations of discretizations using the [https://www.scipy.org NumPy/SciPy] scientific computing stack are implemented to provide an easy to use sandbox for experimentation with new model reduction approaches. '''pyMOR''' offers:		Pure Python implementations of discretizations using the [https://www.scipy.org NumPy/SciPy] scientific computing stack are implemented to provide an easy-to-use sandbox for experimentation with new model reduction approaches. '''pyMOR''' offers:
	* Structured 1D and 2D grids, as well as an experimental Gmsh-based grid, implementing the same abstract grid interface.		* Structured 1D and 2D grids, as well as an experimental Gmsh-based grid, implementing the same abstract grid interface.
	* [[:Wikipedia:Finite_element\|Finite element]] and [[:Wikipedia:Finite_volume\|finite volume]] operators based on this interface.		* [[:Wikipedia:Finite_element\|Finite element]] and [[:Wikipedia:Finite_volume\|finite volume]] operators based on this interface.
	* SciPy/[http://crd-legacy.lbl.gov/~xiaoye/SuperLU SuperLU] based iterative and direct solvers for sparse systems.		* SciPy/[http://crd-legacy.lbl.gov/~xiaoye/SuperLU SuperLU] based iterative and direct solvers for sparse systems.
	* Algebraic multigrid solvers through pyAMG bindings.
	* [[:Wikipedia:Opengl\|OpenGL]] and [http://matplotlib.org matplotlib] based visualizations of solutions.		* [[:Wikipedia:Opengl\|OpenGL]] and [http://matplotlib.org matplotlib] based visualizations of solutions.