As featured in the Journal of Hydraulic Research Paper "Implications of the selection of a particular modal decomposition technique for the analysis of shallow flows". This algorithm connects the spatially orthogonal Proper Orthogonal Decomposition with the temporally orthogonal Dynamic Mode Decomposition. From flow visualisation images, PIV or CFD vector fields it is possible to extract coherent structures which are quasi-spatially and temporally orthogonal.
Two example cases are included with the script. Please cite:
Higham, J.E., Brevis, W & Keylock, C.J. (2018) - Implications of the selection of a particular modal decomposition technique for the analysis of shallow flows , Journal of Hydraulic Research, DOI:10.1080/00221686.2017.1419990
https://www.researchgate.net/publication/322722097_Implications_of_the_selection_of_a_particular_modal_decomposition_technique_for_the_analysis_of_shallow_flows
Cite As
Jonathan Higham (2024). POD_DMD (https://github.com/jonnyhigham/POD_DMD), GitHub. Retrieved .
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- AI, Data Science, and Statistics > Statistics and Machine Learning Toolbox > Dimensionality Reduction and Feature Extraction >
- Sciences > Physics > Fluid Dynamics >
- Engineering > Mechanical Engineering > Computational Fluid Dynamics (CFD) >
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Versions that use the GitHub default branch cannot be downloaded
Version | Published | Release Notes | |
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1.2.0.1 | Added to github |
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1.2.0.0 | Small change to script. For this case no change in functionality.
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1.1.0.0 | Change to syntax as suggested by Yonatan Cadavid. There is no affect to the functionality.
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1.0.0.0 |
Change to text
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