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<registrant>The Open Journal</registrant> | ||
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<full_title>Journal of Open Source Software</full_title> | ||
<abbrev_title>JOSS</abbrev_title> | ||
<issn media_type="electronic">2475-9066</issn> | ||
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<doi>10.21105/joss</doi> | ||
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<title>MODULO: A Python toolbox for data-driven modal | ||
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<article_title>Spectral proper orthogonal | ||
decomposition</article_title> | ||
<author>Sieber</author> | ||
<journal_title>Journal of Fluid Mechanics</journal_title> | ||
<volume>792</volume> | ||
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<unstructured_citation>Sieber, M., Paschereit, C. O., & | ||
Oberleithner, K. (2016). Spectral proper orthogonal decomposition. | ||
Journal of Fluid Mechanics, 792, 798–828. | ||
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<article_title>POD preprocessing of IR thermal data to | ||
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<journal_title>Experimental Mechanics</journal_title> | ||
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D., & Chrysochoos, A. (2014). POD preprocessing of IR thermal data | ||
to assess heat source distributions. Experimental Mechanics, 55, | ||
725–739. | ||
https://doi.org/10.1007/s11340-014-9858-2</unstructured_citation> | ||
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<article_title>MODULO: A software for multiscale proper | ||
orthogonal decomposition of data</article_title> | ||
<author>Ninni</author> | ||
<journal_title>SoftwareX</journal_title> | ||
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<doi>10.1016/j.softx.2020.100622</doi> | ||
<cYear>2020</cYear> | ||
<unstructured_citation>Ninni, D., & Mendez, M. A. | ||
(2020). MODULO: A software for multiscale proper orthogonal | ||
decomposition of data. SoftwareX, 12, 100622. | ||
https://doi.org/10.1016/j.softx.2020.100622</unstructured_citation> | ||
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<article_title>Multi-scale proper orthogonal decomposition | ||
of complex fluid flows</article_title> | ||
<author>Mendez</author> | ||
<journal_title>Journal of Fluid Mechanics</journal_title> | ||
<volume>870</volume> | ||
<doi>10.1017/jfm.2019.212</doi> | ||
<cYear>2019</cYear> | ||
<unstructured_citation>Mendez, M. A., Balabane, M., & | ||
Buchlin, J.-M. (2019). Multi-scale proper orthogonal decomposition of | ||
complex fluid flows. Journal of Fluid Mechanics, 870, 988–1036. | ||
https://doi.org/10.1017/jfm.2019.212</unstructured_citation> | ||
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<article_title>Dynamic mode decomposition of numerical and | ||
experimental data</article_title> | ||
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<journal_title>Journal of Fluid Mechanics</journal_title> | ||
<volume>656</volume> | ||
<doi>10.1017/S0022112010001217</doi> | ||
<cYear>2010</cYear> | ||
<unstructured_citation>Schmid, P. J. (2010). Dynamic mode | ||
decomposition of numerical and experimental data. Journal of Fluid | ||
Mechanics, 656, 5–28. | ||
https://doi.org/10.1017/S0022112010001217</unstructured_citation> | ||
</citation> | ||
<citation key="Towne_2018"> | ||
<article_title>Spectral proper orthogonal decomposition and | ||
its relationship to dynamic mode decomposition and resolvent | ||
analysis</article_title> | ||
<author>Towne</author> | ||
<journal_title>Journal of Fluid Mechanics</journal_title> | ||
<volume>847</volume> | ||
<doi>10.1017/jfm.2018.283</doi> | ||
<cYear>2018</cYear> | ||
<unstructured_citation>Towne, A., Schmidt, O. T., & | ||
Colonius, T. (2018). Spectral proper orthogonal decomposition and its | ||
relationship to dynamic mode decomposition and resolvent analysis. | ||
Journal of Fluid Mechanics, 847, 821–867. | ||
https://doi.org/10.1017/jfm.2018.283</unstructured_citation> | ||
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<article_title>Linear and nonlinear dimensionality reduction | ||
from fluid mechanics to machine learning</article_title> | ||
<author>Mendez</author> | ||
<journal_title>Measurement Science and | ||
Technology</journal_title> | ||
<volume>34</volume> | ||
<doi>10.1088/1361-6501/acaffe</doi> | ||
<cYear>2023</cYear> | ||
<unstructured_citation>Mendez, M. A. (2023). Linear and | ||
nonlinear dimensionality reduction from fluid mechanics to machine | ||
learning. Measurement Science and Technology, 34, 042001. | ||
https://doi.org/10.1088/1361-6501/acaffe</unstructured_citation> | ||
</citation> | ||
<citation key="Taira2020"> | ||
<article_title>Modal analysis of fluid flows: Applications | ||
and outlook</article_title> | ||
<author>Taira</author> | ||
<journal_title>AIAA Journal</journal_title> | ||
<issue>3</issue> | ||
<volume>58</volume> | ||
<doi>10.2514/1.J058462</doi> | ||
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<unstructured_citation>Taira, K., Hemati, M. S., Brunton, S. | ||
L., Sun, Y., Duraisamy, K., Bagheri, S., Dawson, S. T. M., & Yeh, | ||
C.-A. (2020). Modal analysis of fluid flows: Applications and outlook. | ||
AIAA Journal, 58(3), 998–1022. | ||
https://doi.org/10.2514/1.J058462</unstructured_citation> | ||
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<citation key="py_DMD"> | ||
<article_title>PyDMD: Python dynamic mode | ||
decomposition</article_title> | ||
<author>Demo</author> | ||
<journal_title>Journal of Open Source | ||
Software</journal_title> | ||
<issue>22</issue> | ||
<volume>3</volume> | ||
<doi>10.21105/joss.00530</doi> | ||
<cYear>2018</cYear> | ||
<unstructured_citation>Demo, N., Tezzele, M., & Rozza, | ||
G. (2018). PyDMD: Python dynamic mode decomposition. Journal of Open | ||
Source Software, 3(22), 530. | ||
https://doi.org/10.21105/joss.00530</unstructured_citation> | ||
</citation> | ||
<citation key="Mengaldo2021"> | ||
<article_title>PySPOD: A Python package for Spectral Proper | ||
Orthogonal Decomposition (SPOD)</article_title> | ||
<author>Mengaldo</author> | ||
<journal_title>Journal of Open Source | ||
Software</journal_title> | ||
<issue>60</issue> | ||
<volume>6</volume> | ||
<doi>10.21105/joss.02862</doi> | ||
<cYear>2021</cYear> | ||
<unstructured_citation>Mengaldo, G., & Maulik, R. | ||
(2021). PySPOD: A Python package for Spectral Proper Orthogonal | ||
Decomposition (SPOD). Journal of Open Source Software, 6(60), 2862. | ||
https://doi.org/10.21105/joss.02862</unstructured_citation> | ||
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<citation key="SpyOD"> | ||
<article_title>Spectral proper orthogonal | ||
decomposition</article_title> | ||
<author>Hatzissawidis</author> | ||
<cYear>2023</cYear> | ||
<unstructured_citation>Hatzissawidis, G., & Sieber, M. | ||
(2023). Spectral proper orthogonal decomposition. | ||
https://github.com/grigorishat/SPyOD.</unstructured_citation> | ||
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<citation key="rogowski2024unlocking"> | ||
<article_title>Unlocking massively parallel spectral proper | ||
orthogonal decompositions in the PySPOD package</article_title> | ||
<author>Rogowski</author> | ||
<journal_title>Computer Physics | ||
Communications</journal_title> | ||
<volume>302</volume> | ||
<doi>10.1016/j.cpc.2024.109246</doi> | ||
<issn>0010-4655</issn> | ||
<cYear>2024</cYear> | ||
<unstructured_citation>Rogowski, M., Yeung, B. C. Y., | ||
Schmidt, O. T., Maulik, R., Dalcin, L., Parsani, M., & Mengaldo, G. | ||
(2024). Unlocking massively parallel spectral proper orthogonal | ||
decompositions in the PySPOD package. Computer Physics Communications, | ||
302, 109246. | ||
https://doi.org/10.1016/j.cpc.2024.109246</unstructured_citation> | ||
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