State-Of-The-Art Algorithms For Low-Rank Dynamic Mode Decomposition
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Abstract (arXiv)
This technical note reviews sate-of-the-art algorithms for linear approximation of high-dimensional dynamical systems using low-rank dynamic mode decomposition (DMD). While repeating several parts of our article "low-rank dynamic mode decomposition: an exact and tractable solution", this work provides additional details useful for building a comprehensive picture of state-of-the-art methods.
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References · 5
- 2017Learning Deep Neural Network Representations for Koopman Operators of Nonlinear Dynamical Systemscited by 125
- 2014Data-Driven Model Reduction for the Bayesian Solution of Inverse Problemscited by 20
- 2016Low-Rank Dynamic Mode Decomposition: An Exact and Tractable Solutioncited by 2
- 2018Low-rank Approximation of Linear Mapscited by 1
- 2020Generalized Kernel-Based Dynamic Mode Decompositioncited by 1
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