diff --git a/joss.06753/10.21105.joss.06753.crossref.xml b/joss.06753/10.21105.joss.06753.crossref.xml new file mode 100644 index 0000000000..a72722ea81 --- /dev/null +++ b/joss.06753/10.21105.joss.06753.crossref.xml @@ -0,0 +1,274 @@ + + + + 20241017163917-4f4fe0a5a17aadd329d49a1d4b8b2a9d29d99cdc + 20241017163917 + + JOSS Admin + admin@theoj.org + + The Open Journal + + + + + Journal of Open Source Software + JOSS + 2475-9066 + + 10.21105/joss + https://joss.theoj.org + + + + + 10 + 2024 + + + 9 + + 102 + + + + MODULO: A Python toolbox for data-driven modal +decomposition + + + + R. + Poletti + https://orcid.org/0000-0003-3566-6956 + + + L. + Schena + https://orcid.org/0000-0002-7183-0242 + + + D. + Ninni + https://orcid.org/0000-0002-7179-3322 + + + M. A. + Mendez + https://orcid.org/0000-0002-1115-2187 + + + + 10 + 17 + 2024 + + + 6753 + + + 10.21105/joss.06753 + + + http://creativecommons.org/licenses/by/4.0/ + http://creativecommons.org/licenses/by/4.0/ + http://creativecommons.org/licenses/by/4.0/ + + + + Software archive + 10.5281/zenodo.13939520 + + + GitHub review issue + https://github.com/openjournals/joss-reviews/issues/6753 + + + + 10.21105/joss.06753 + https://joss.theoj.org/papers/10.21105/joss.06753 + + + https://joss.theoj.org/papers/10.21105/joss.06753.pdf + + + + + + Spectral proper orthogonal +decomposition + Sieber + Journal of Fluid Mechanics + 792 + 10.1017/jfm.2016.103 + 2016 + Sieber, M., Paschereit, C. O., & +Oberleithner, K. (2016). Spectral proper orthogonal decomposition. +Journal of Fluid Mechanics, 792, 798–828. +https://doi.org/10.1017/jfm.2016.103 + + + POD preprocessing of IR thermal data to +assess heat source distributions + Ranc + Experimental Mechanics + 55 + 10.1007/s11340-014-9858-2 + 2014 + Ranc, N., Blanche, A., Ryckelynck, +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 + + + MODULO: A software for multiscale proper +orthogonal decomposition of data + Ninni + SoftwareX + 12 + 10.1016/j.softx.2020.100622 + 2020 + 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 + + + Multi-scale proper orthogonal decomposition +of complex fluid flows + Mendez + Journal of Fluid Mechanics + 870 + 10.1017/jfm.2019.212 + 2019 + 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 + + + Dynamic mode decomposition of numerical and +experimental data + Schmid + Journal of Fluid Mechanics + 656 + 10.1017/S0022112010001217 + 2010 + 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 + + + Spectral proper orthogonal decomposition and +its relationship to dynamic mode decomposition and resolvent +analysis + Towne + Journal of Fluid Mechanics + 847 + 10.1017/jfm.2018.283 + 2018 + 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 + + + Linear and nonlinear dimensionality reduction +from fluid mechanics to machine learning + Mendez + Measurement Science and +Technology + 34 + 10.1088/1361-6501/acaffe + 2023 + 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 + + + Modal analysis of fluid flows: Applications +and outlook + Taira + AIAA Journal + 3 + 58 + 10.2514/1.J058462 + 2020 + 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 + + + PyDMD: Python dynamic mode +decomposition + Demo + Journal of Open Source +Software + 22 + 3 + 10.21105/joss.00530 + 2018 + 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 + + + PySPOD: A Python package for Spectral Proper +Orthogonal Decomposition (SPOD) + Mengaldo + Journal of Open Source +Software + 60 + 6 + 10.21105/joss.02862 + 2021 + 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 + + + Spectral proper orthogonal +decomposition + Hatzissawidis + 2023 + Hatzissawidis, G., & Sieber, M. +(2023). Spectral proper orthogonal decomposition. +https://github.com/grigorishat/SPyOD. + + + Unlocking massively parallel spectral proper +orthogonal decompositions in the PySPOD package + Rogowski + Computer Physics +Communications + 302 + 10.1016/j.cpc.2024.109246 + 0010-4655 + 2024 + 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 + + + + + +