PyDMD - Revision history

Himpe: remove UC

2019-02-11T17:00:26Z

remove UC

← Older revision		Revision as of 17:00, 11 February 2019
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	~~{{preliminary}} <!-- Do not remove -->~~

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

Himpe: /* References */ fix links.

2019-01-22T12:03:23Z

References: fix links.

← Older revision		Revision as of 12:03, 22 January 2019
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	<references>		<references>

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

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

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

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

	</references>		</references>

Demo at 10:22, 22 January 2019

2019-01-22T10:22:45Z

← Older revision		Revision as of 10:22, 22 January 2019
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	@article{demo18pydmd,		@article{demo18pydmd,
	Author = { Demo, Nicola and Tezzele, Marco and Rozza, Gianluigi },		Author = {Demo, Nicola and Tezzele, Marco and Rozza, Gianluigi},
	Title = { PyDMD: Python Dynamic Mode Decomposition },		Title = {{PyDMD}: Python Dynamic Mode Decomposition},
	Journal = { The Journal of Open Source Software },		Journal = {The Journal of Open Source Software},
	Volume = { 3 },		Volume = {3},
	Number = { 22 },		Number = {22},
	Pages = { 530 },		Pages = {530},
	Year = { 2018 },		Year = {2018},
	Doi = { <span class="plainlinks">[https://doi.org/10.21105/joss.00530: ~~https://doi.org/~~10.21105/joss.00530]</span> }		Doi = {<span class="plainlinks">[https://doi.org/10.21105/joss.00530: 10.21105/joss.00530]</span>}
	}		}

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	<ref name="schmid">P. Schmid, "<span class="plainlinks">[https://doi.org/10.1017/S0022112010001217: Dynamic mode decomposition of numerical and experimental data]</span>", Journal of Fluid Mechanics, 656, 5-28, 2010</ref>		<ref name="schmid">P. Schmid, "<span class="plainlinks">[https://doi.org/10.1017/S0022112010001217: Dynamic mode decomposition of numerical and experimental data]</span>", 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, "<span class="plainlinks">[https://doi.org/10.1137/1.9781611974508: Dynamic Mode Decomposition: Data-Driven Modeling of Complex Systems]</span>", SIAM, 2016</ref>		<ref name="kutz">J. N. Kutz, S. L. Brunton, B. W. Brunton, J. L. Proctor, "<span class="plainlinks">[https://doi.org/10.1137/1.9781611974508: Dynamic Mode Decomposition: Data-Driven Modeling of Complex Systems]</span>", Vol. 149, SIAM, 2016</ref>

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

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

	</references>		</references>

Demo at 17:27, 21 January 2019

2019-01-21T17:27:09Z

← Older revision		Revision as of 17:27, 21 January 2019
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	== Links ==		== Links ==
	* <span class="plainlinks">[https://github.com/mathLab/PyDMD: Official software page]</span>		* <span class="plainlinks">[https://github.com/mathLab/PyDMD Official software page]</span>
	* <span class="plainlinks">[https://mathlab.github.io/PyDMD: Online documentation]</span>		* <span class="plainlinks">[https://mathlab.github.io/PyDMD Online documentation]</span>

	== References ==		== References ==

Demo: references and links

2019-01-21T17:26:19Z

references and links

← Older revision		Revision as of 17:26, 21 January 2019
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	[[file:Logo_pydmd.png‎\|200px\|right\|PyDMD logo]]		[[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.		[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.		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.
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	== Features ==		== Features ==
	The following DMD versions are available in the latest release of the software (as of January 2019):		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]~~		* Standard DMD
	* Multi-resolution DMD ~~[https://mathlab.github.io/PyDMD/mrdmd.html]~~		* Multi-resolution DMD
	* Compressed DMD ~~[https://mathlab.github.io/PyDMD/cdmd.html]~~		* Compressed DMD
	* DMD with control ~~[https://mathlab.github.io/PyDMD/dmdc.html]~~		* DMD with control
	* Forward-backward DMD ~~[https://mathlab.github.io/PyDMD/fbdmd.html]~~		* Forward-backward DMD
	* Higher order DMD ~~[https://mathlab.github.io/PyDMD/hodmd.html]~~		* Higher order DMD
	(Exact DMD, projected DMD and optimized DMD are available for all the versions)		(Exact DMD, projected DMD and optimized DMD are available for all the versions)


	The following features are also available:		The following features are also available:
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	* Options for SVD truncation and total least square denoising;		* Options for SVD truncation and total least square denoising;
	* Several tutorials to show typical usecases.		* 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 = { <span class="plainlinks">[https://doi.org/10.21105/joss.00530: https://doi.org/10.21105/joss.00530]</span> }
			}

			== Links ==
			* <span class="plainlinks">[https://github.com/mathLab/PyDMD: Official software page]</span>
			* <span class="plainlinks">[https://mathlab.github.io/PyDMD: Online documentation]</span>

	== References ==		== 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]~~		<references>

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

	== ~~Links ==~~		<ref name="kutz">J. N. Kutz, S. L. Brunton, B. W. Brunton, and J. L. Proctor, "<span class="plainlinks">[https://doi.org/10.1137/1.9781611974508: Dynamic Mode Decomposition: Data-Driven Modeling of Complex Systems]</span>", SIAM, 2016</ref>
	* 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]~~		<ref name="koopman">B. O. Koopman, "<span class="plainlinks">[https://doi.org/10.1073/pnas.17.5.315: Hamiltonian Systems and Transformation in Hilbert Space]</span>", PNAS, 17 (5), 315-318, 1931 </ref>

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

			</references>

	== Contact ==		== Contact ==

Demo at 09:44, 16 January 2019

2019-01-16T09:44:28Z

@@ Line 7: / Line 7: @@
 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]]

Demo at 09:20, 16 January 2019

2019-01-16T09:20:01Z

← Older revision		Revision as of 09:20, 16 January 2019
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	{{preliminary}} <!-- Do not remove -->		{{preliminary}} <!-- Do not remove -->

			[[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.		[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.

Demo at 09:14, 16 January 2019

2019-01-16T09:14:09Z

← Older revision		Revision as of 09:14, 16 January 2019
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	{{preliminary}} <!-- Do not remove -->		{{preliminary}} <!-- Do not remove -->
			[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:Software]]
			[[Category:Python]]

Himpe: init PyDMD

2019-01-14T14:15:45Z

init PyDMD

New page

{{preliminary}} 

[[Category:Software]]