State-Of-The-Art Algorithms For Low-Rank Dynamic Mode Decomposition
Machine Learning
2021-08-23 v1 Machine Learning
Abstract
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.
Keywords
Cite
@article{arxiv.2108.09160,
title = {State-Of-The-Art Algorithms For Low-Rank Dynamic Mode Decomposition},
author = {Patrick Heas and Cedric Herzet},
journal= {arXiv preprint arXiv:2108.09160},
year = {2021}
}
Comments
arXiv admin note: substantial text overlap with arXiv:1610.02962