MOR Wiki - User contributions [en]

PyDMD

2019-01-22T10:22:45Z

Demo:

{{preliminary}} 

[[file:Logo_pydmd.png‎|200px|right|PyDMD logo]]

[https://github.com/mathLab/PyDMD PyDMD] is a Python package that uses Dynamic Mode Decomposition (DMD) for a data-driven model simplification based on spatiotemporal coherent structures. DMD is a model reduction algorithm developed by Schmid <ref name="schmid"/>. Since then has emerged as a powerful tool for analyzing the dynamics of nonlinear systems. DMD relies only on the high-fidelity measurements, like experimental data and numerical simulations, so it is an equation-free algorithm. Its popularity is also due to the fact that it does not make any assumptions about the underlying system. See <ref name="kutz"/> for a comprehensive overview of the algorithm and its connections to the Koopman-operator analysis, initiated in <ref name="koopman"/>, along with examples in computational fluid dynamics.

In PyDMD we implemented the majority of the variants mentioned above with a user friendly interface. Moreover, we generated examples and tutorials to show the software capabilities.

== Features ==
The following DMD versions are available in the latest release of the software (as of January 2019):
* Standard DMD
* Multi-resolution DMD
* Compressed DMD
* DMD with control
* Forward-backward DMD
* Higher order DMD
(Exact DMD, projected DMD and optimized DMD are available for all the versions)

The following features are also available:
* Manipulation of the temporal window for the reconstructed system, allowing to interpolate/extrapolate the system dynamics;
* Options for SVD truncation and total least square denoising;
* Several tutorials to show typical usecases.

== Citation ==

If you use this package in your publications please cite the package as follows:

* Nicola Demo, Marco Tezzele, Gianluigi Rozza (2018). PyDMD: Python Dynamic Mode Decomposition. Journal of Open Source Software, 3(22), 530 <ref name="pydmd"/>

Or if you use LaTeX:

@article{demo18pydmd,
Author = {Demo, Nicola and Tezzele, Marco and Rozza, Gianluigi},
Title = {{PyDMD}: Python Dynamic Mode Decomposition},
Journal = {The Journal of Open Source Software},
Volume = {3},
Number = {22},
Pages = {530},
Year = {2018},
Doi = {[https://doi.org/10.21105/joss.00530: 10.21105/joss.00530]}
}

== Links ==
* [https://github.com/mathLab/PyDMD Official software page]
* [https://mathlab.github.io/PyDMD Online documentation]

== References ==
<references>

<ref name="schmid">P. Schmid, "[https://doi.org/10.1017/S0022112010001217: Dynamic mode decomposition of numerical and experimental data]", Journal of Fluid Mechanics, 656, 5-28, 2010</ref>

<ref name="kutz">J. N. Kutz, S. L. Brunton, B. W. Brunton, J. L. Proctor, "[https://doi.org/10.1137/1.9781611974508: Dynamic Mode Decomposition: Data-Driven Modeling of Complex Systems]", Vol. 149, SIAM, 2016</ref>

<ref name="koopman">B. O. Koopman, "[https://doi.org/10.1073/pnas.17.5.315: Hamiltonian Systems and Transformation in Hilbert Space]", PNAS, 17 (5), 315-318, 1931 </ref>

<ref name="pydmd">N. Demo, M. Tezzele, G. Rozza, "[https://doi.org/10.21105/joss.00530: PyDMD: Python Dynamic Mode Decomposition]", Journal of Open Source Software, 3(22), 530, 2018</ref>

</references>

== Contact ==
* [[User:Demo|Nicola Demo]]
* [[User:Tezzele|Marco Tezzele]]

[[Category:Software]]
[[Category:Python]]

PyDMD

2019-01-21T17:27:09Z

Demo:

{{preliminary}} 

[[file:Logo_pydmd.png‎|200px|right|PyDMD logo]]

[https://github.com/mathLab/PyDMD PyDMD] is a Python package that uses Dynamic Mode Decomposition (DMD) for a data-driven model simplification based on spatiotemporal coherent structures. DMD is a model reduction algorithm developed by Schmid <ref name="schmid"/>. Since then has emerged as a powerful tool for analyzing the dynamics of nonlinear systems. DMD relies only on the high-fidelity measurements, like experimental data and numerical simulations, so it is an equation-free algorithm. Its popularity is also due to the fact that it does not make any assumptions about the underlying system. See <ref name="kutz"/> for a comprehensive overview of the algorithm and its connections to the Koopman-operator analysis, initiated in <ref name="koopman"/>, along with examples in computational fluid dynamics.

In PyDMD we implemented the majority of the variants mentioned above with a user friendly interface. Moreover, we generated examples and tutorials to show the software capabilities.

== Features ==
The following DMD versions are available in the latest release of the software (as of January 2019):
* Standard DMD
* Multi-resolution DMD
* Compressed DMD
* DMD with control
* Forward-backward DMD
* Higher order DMD
(Exact DMD, projected DMD and optimized DMD are available for all the versions)

The following features are also available:
* Manipulation of the temporal window for the reconstructed system, allowing to interpolate/extrapolate the system dynamics;
* Options for SVD truncation and total least square denoising;
* Several tutorials to show typical usecases.

== Citation ==

If you use this package in your publications please cite the package as follows:

* Nicola Demo, Marco Tezzele, Gianluigi Rozza (2018). PyDMD: Python Dynamic Mode Decomposition. Journal of Open Source Software, 3(22), 530 <ref name="pydmd"/>

Or if you use LaTeX:

@article{demo18pydmd,
Author = { Demo, Nicola and Tezzele, Marco and Rozza, Gianluigi },
Title = { PyDMD: Python Dynamic Mode Decomposition },
Journal = { The Journal of Open Source Software },
Volume = { 3 },
Number = { 22 },
Pages = { 530 },
Year = { 2018 },
Doi = { [https://doi.org/10.21105/joss.00530: https://doi.org/10.21105/joss.00530] }
}

== Links ==
* [https://github.com/mathLab/PyDMD Official software page]
* [https://mathlab.github.io/PyDMD Online documentation]

== References ==
<references>

<ref name="schmid">P. Schmid, "[https://doi.org/10.1017/S0022112010001217: Dynamic mode decomposition of numerical and experimental data]", Journal of Fluid Mechanics, 656, 5-28, 2010</ref>

<ref name="kutz">J. N. Kutz, S. L. Brunton, B. W. Brunton, and J. L. Proctor, "[https://doi.org/10.1137/1.9781611974508: Dynamic Mode Decomposition: Data-Driven Modeling of Complex Systems]", SIAM, 2016</ref>

<ref name="koopman">B. O. Koopman, "[https://doi.org/10.1073/pnas.17.5.315: Hamiltonian Systems and Transformation in Hilbert Space]", PNAS, 17 (5), 315-318, 1931 </ref>

<ref name="pydmd">N. Demo, M. Tezzele, G. Rozza, "[https://doi.org/10.21105/joss.00530: Python Dynamic Mode Decomposition. ]", Journal of Open Source Software, 3(22), 530, 2018</ref>

</references>

== Contact ==
* [[User:Demo|Nicola Demo]]
* [[User:Tezzele|Marco Tezzele]]

[[Category:Software]]
[[Category:Python]]

PyDMD

2019-01-21T17:26:19Z

Demo: references and links

{{preliminary}} 

[[file:Logo_pydmd.png‎|200px|right|PyDMD logo]]

[https://github.com/mathLab/PyDMD PyDMD] is a Python package that uses Dynamic Mode Decomposition (DMD) for a data-driven model simplification based on spatiotemporal coherent structures. DMD is a model reduction algorithm developed by Schmid <ref name="schmid"/>. Since then has emerged as a powerful tool for analyzing the dynamics of nonlinear systems. DMD relies only on the high-fidelity measurements, like experimental data and numerical simulations, so it is an equation-free algorithm. Its popularity is also due to the fact that it does not make any assumptions about the underlying system. See <ref name="kutz"/> for a comprehensive overview of the algorithm and its connections to the Koopman-operator analysis, initiated in <ref name="koopman"/>, along with examples in computational fluid dynamics.

In PyDMD we implemented the majority of the variants mentioned above with a user friendly interface. Moreover, we generated examples and tutorials to show the software capabilities.

== Features ==
The following DMD versions are available in the latest release of the software (as of January 2019):
* Standard DMD
* Multi-resolution DMD
* Compressed DMD
* DMD with control
* Forward-backward DMD
* Higher order DMD
(Exact DMD, projected DMD and optimized DMD are available for all the versions)

The following features are also available:
* Manipulation of the temporal window for the reconstructed system, allowing to interpolate/extrapolate the system dynamics;
* Options for SVD truncation and total least square denoising;
* Several tutorials to show typical usecases.

== Citation ==

If you use this package in your publications please cite the package as follows:

* Nicola Demo, Marco Tezzele, Gianluigi Rozza (2018). PyDMD: Python Dynamic Mode Decomposition. Journal of Open Source Software, 3(22), 530 <ref name="pydmd"/>

Or if you use LaTeX:

@article{demo18pydmd,
Author = { Demo, Nicola and Tezzele, Marco and Rozza, Gianluigi },
Title = { PyDMD: Python Dynamic Mode Decomposition },
Journal = { The Journal of Open Source Software },
Volume = { 3 },
Number = { 22 },
Pages = { 530 },
Year = { 2018 },
Doi = { [https://doi.org/10.21105/joss.00530: https://doi.org/10.21105/joss.00530] }
}

== Links ==
* [https://github.com/mathLab/PyDMD: Official software page]
* [https://mathlab.github.io/PyDMD: Online documentation]

== References ==
<references>

<ref name="schmid">P. Schmid, "[https://doi.org/10.1017/S0022112010001217: Dynamic mode decomposition of numerical and experimental data]", Journal of Fluid Mechanics, 656, 5-28, 2010</ref>

<ref name="kutz">J. N. Kutz, S. L. Brunton, B. W. Brunton, and J. L. Proctor, "[https://doi.org/10.1137/1.9781611974508: Dynamic Mode Decomposition: Data-Driven Modeling of Complex Systems]", SIAM, 2016</ref>

<ref name="koopman">B. O. Koopman, "[https://doi.org/10.1073/pnas.17.5.315: Hamiltonian Systems and Transformation in Hilbert Space]", PNAS, 17 (5), 315-318, 1931 </ref>

<ref name="pydmd">N. Demo, M. Tezzele, G. Rozza, "[https://doi.org/10.21105/joss.00530: Python Dynamic Mode Decomposition. ]", Journal of Open Source Software, 3(22), 530, 2018</ref>

</references>

== Contact ==
* [[User:Demo|Nicola Demo]]
* [[User:Tezzele|Marco Tezzele]]

[[Category:Software]]
[[Category:Python]]

PyDMD

2019-01-16T09:44:28Z

Demo:

{{preliminary}} 

[[file:Logo_pydmd.png‎|200px|right|PyDMD logo]]

[https://github.com/mathLab/PyDMD PyDMD] is a Python package that uses Dynamic Mode Decomposition (DMD) for a data-driven model simplification based on spatiotemporal coherent structures. DMD is a model reduction algorithm developed by Schmid [https://doi.org/10.1017/S0022112010001217]. Since then has emerged as a powerful tool for analyzing the dynamics of nonlinear systems. DMD relies only on the high-fidelity measurements, like experimental data and numerical simulations, so it is an equation-free algorithm. Its popularity is also due to the fact that it does not make any assumptions about the underlying system. See [https://doi.org/10.1137/1.9781611974508] for a comprehensive overview of the algorithm and its connections to the Koopman-operator analysis, initiated in [https://doi.org/10.1073/pnas.17.5.315], along with examples in computational fluid dynamics.

In PyDMD we implemented the majority of the variants mentioned above with a user friendly interface. Moreover, we generated examples and tutorials to show the software capabilities.

== Features ==
The following DMD versions are available in the latest release of the software (as of January 2019):
* Standard DMD [https://mathlab.github.io/PyDMD/dmd.html]
* Multi-resolution DMD [https://mathlab.github.io/PyDMD/mrdmd.html]
* Compressed DMD [https://mathlab.github.io/PyDMD/cdmd.html]
* DMD with control [https://mathlab.github.io/PyDMD/dmdc.html]
* Forward-backward DMD [https://mathlab.github.io/PyDMD/fbdmd.html]
* Higher order DMD [https://mathlab.github.io/PyDMD/hodmd.html]
(Exact DMD, projected DMD and optimized DMD are available for all the versions)

The following features are also available:
* Manipulation of the temporal window for the reconstructed system, allowing to interpolate/extrapolate the system dynamics;
* Options for SVD truncation and total least square denoising;
* Several tutorials to show typical usecases.

== References ==
* Nicola Demo, Marco Tezzele, Gianluigi Rozza (2018). PyDMD: Python Dynamic Mode Decomposition. Journal of Open Source Software, 3(22), 530, [https://doi.org/10.21105/joss.00530 https://doi.org/10.21105/joss.00530]

== Links ==
* Official software page: [https://github.com/mathLab/PyDMD https://github.com/mathLab/PyDMD]
* Online documentation: [https://mathlab.github.io/PyDMD https://mathlab.github.io/PyDMD]

== Contact ==
* [[User:Demo|Nicola Demo]]
* [[User:Tezzele|Marco Tezzele]]

[[Category:Software]]
[[Category:Python]]

PyDMD

2019-01-16T09:20:01Z

Demo:

File:Logo pydmd.png

2019-01-16T09:18:42Z

Demo:

PyDMD

2019-01-16T09:14:09Z

Demo:

{{preliminary}} 
[https://github.com/mathLab/PyDMD PyDMD] is a Python package that uses Dynamic Mode Decomposition (DMD) for a data-driven model simplification based on spatiotemporal coherent structures. DMD is a model reduction algorithm developed by Schmid [https://doi.org/10.1017/S0022112010001217]. Since then has emerged as a powerful tool for analyzing the dynamics of nonlinear systems. DMD relies only on the high-fidelity measurements, like experimental data and numerical simulations, so it is an equation-free algorithm. Its popularity is also due to the fact that it does not make any assumptions about the underlying system. See [https://doi.org/10.1137/1.9781611974508] for a comprehensive overview of the algorithm and its connections to the Koopman-operator analysis, initiated in [https://doi.org/10.1073/pnas.17.5.315], along with examples in computational fluid dynamics.

In PyDMD we implemented the majority of the variants mentioned above with a user friendly interface. Moreover, we generated examples and tutorials to show the software capabilities.
[[Category:Software]]
[[Category:Python]]

User:Demo

2019-01-15T16:42:20Z

Demo:

{{preliminary}} 

Nicola Demo
----------------------------------------

Mathematics Area, mathLab, SISSA, International School of Advanced Studies 
via Bonomea 265, I-34136 
Trieste, Italy 



email: [mailto:ndemo@sissa.it ndemo@sissa.it] 
website: [https://people.sissa.it/~ndemo/ people.sissa.it/~ndemo]

User:Demo

2019-01-15T16:42:03Z

Demo: Created page with "{{preliminary}}  Nicola Demo ---------------------------------------- Mathematics Area, mathLab, SISSA, International School of Advanced Studies ..."

{{preliminary}} 

Nicola Demo
----------------------------------------

Mathematics Area, mathLab, SISSA, International School of Advanced Studies 
via Bonomea 265, I-34136 
Trieste, Italy 



email:[mailto:ndemo@sissa.it ndemo@sissa.it] 
website: [https://people.sissa.it/~ndemo/ people.sissa.it/~ndemo]