Useful References

[1]

Romain Poletti, L Schena, D Ninni, and Miguel Alfonso Mendez. Modulo: a python toolbox for data-driven modal decomposition. Journal of Open Source Software, 9(102):6753, 2024.

[2]

Miguel A. Mendez. Linear and nonlinear dimensionality reduction from fluid mechanics to machine learning. Measurement Science and Technology, 34:042001, January 2023. doi:10.1088/1361-6501/acaffe.

[3]

Davide Ninni and Miguel A. Mendez. Modulo: a software for multiscale proper orthogonal decomposition of data. SoftwareX, 12:100622, 12 2020. doi:10.1016/j.softx.2020.100622.

[4]

Peter J. Schmid. Dynamic mode decomposition of numerical and experimental data. Journal of Fluid Mechanics, 656:5–28, 2010. doi:10.1017/S0022112010001217.

[5]

Jonathan H Tu. Dynamic mode decomposition: Theory and applications. PhD thesis, Princeton University, 2013.

[6]

Miguel Alfonso Mendez. Statistical treatment, fourier and modal decomposition. arXiv preprint arXiv:2201.03847, 2022.

[7]

Moritz Sieber, C. Oliver Paschereit, and Kilian Oberleithner. Spectral proper orthogonal decomposition. Journal of Fluid Mechanics, 792:798–828, 2016. doi:10.1017/jfm.2016.103.

[8]

Aaron Towne, Oliver T. Schmidt, and Tim Colonius. Spectral proper orthogonal decomposition and its relationship to dynamic mode decomposition and resolvent analysis. Journal of Fluid Mechanics, 847:821–867, 2018. doi:10.1017/jfm.2018.283.