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Published in Physics of Fluids, 2019
This paper is about the new method of composite DMD which could find the most relative modes to the physics.
Recommended citation: Jesús Garicano-Mena, Binghua Li, Esteban Ferrer, Eusebio Valero. (2019) "A composite dynamic mode decomposition analysis of turbulent channel flows" Physics of Fluids. 31(11):115102. http://dx.doi.org/10.1063/1.5119342
Published in Energies, 2020
This paper is about using machine learning and compress sensing theory to apply DMD on large flow datasets.
Recommended citation: Binghua Li, Jesús Garicano-Mena, Yao Zheng, Eusebio Valero. (2020) "Dynamic Mode Decomposition Analysis of Spatially Agglomerated Flow Databases" Energies. 13(9):2134. http://dx.doi.org/10.3390/en13092134
Published in Journal of Computational Physics, 2022
This paper is about the novel method of $\theta$-DMD, which can handle non-uniformly sampled data.
Recommended citation: Binghua Li, Jesús Garicano-Mena, Eusebio Valero. (2022) "A dynamic mode decomposition technique for the analysis of non–uniformly sampled flow data" Journal of Computational Physics. 468(3):111495. http://dx.doi.org/10.1016/j.jcp.2022.111495
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Undergraduate course, University 1, Department, 2014
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Workshop, University 1, Department, 2015
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