Optimal low-rank Dynamic Mode Decomposition

@article{Has2017OptimalLD,
  title={Optimal low-rank Dynamic Mode Decomposition},
  author={P. H{\'e}as and C. Herzet},
  journal={2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  year={2017},
  pages={4456-4460}
}
  • P. Héas, C. Herzet
  • Published 2017
  • Computer Science, Mathematics
  • 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
  • Dynamic Mode Decomposition (DMD) has emerged as a powerful tool for analyzing the dynamics of non-linear systems from experimental datasets. Recently, several attempts have extended DMD to the context of low-rank approximations. This extension is of particular interest for reduced-order modeling in various applicative domains, e.g., for climate prediction, to study molecular dynamics or microelectromechanical devices. This low-rank extension takes the form of a non-convex optimization problem… CONTINUE READING
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