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References
Brunton BW, Johnson LA, Ojemann JG, Kutz JN (2016) Extracting spatial–temporal coherent patterns in large-scale neural recordings using dynamic mode decomposition. J Neurosci Methods 258:1–15. https://doi.org/10.1016/j.jneumeth.2015.10.010
Demo N, Tezzele M, Rozza G (2018) PyDMD: python dynamic mode decomposition. J Open Sour Softw 3:530. https://doi.org/10.21105/joss.00530
Grosek J, Kutz JN (2014) Dynamic mode decomposition for real-time background/foreground separation in video. arXiv:s14047592 [cs]
Le Clainche S, Vega JM, Soria J (2017) Higher order dynamic mode decomposition of noisy experimental data: the flow structure of a zero-net-mass-flux jet. Exp Therm Fluid Sci 88:336–353. https://doi.org/10.1016/j.expthermflusci.2017.06.011
Mohammadshahi S, Samsam-Khayani H, Kim KC (2022) Experimental investigation on flow characteristics of compressible oscillating jet. Phys Fluids 34:016111. https://doi.org/10.1063/5.0076544
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Binqi Chen and Michael Chukwuemeka Ekwonu are co-first authors.
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Chen, B., Ekwonu, M.C. & Samsam-Khayani, H. Robust modal decomposition of low-resolution schlieren visualization of supersonic flows. J Vis 25, 923–928 (2022). https://doi.org/10.1007/s12650-022-00833-y
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DOI: https://doi.org/10.1007/s12650-022-00833-y