State-Of-The-Art Algorithms For Low-Rank Dynamic Mode Decomposition
arXiv:2108.09160v12 citations
Originality Synthesis-oriented
AI Analysis
It offers a comprehensive overview for researchers in computational science, but is incremental as it builds on prior work.
This paper reviews state-of-the-art algorithms for low-rank dynamic mode decomposition (DMD) to approximate high-dimensional dynamical systems, providing additional details to complement existing literature.
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.