Klus, S. and Gelß, P. and Peitz, S. and Schütte, Ch.
(2018)
*Tensor-based dynamic mode decomposition.*
Nonlinearity, 31
(7).
pp. 3359-3380.
ISSN 0951-7715

Full text not available from this repository.

Official URL: https://iopscience.iop.org/article/10.1088/1361-65...

## Abstract

Dynamic mode decomposition (DMD) is a recently developed tool for the analysis of the behavior of complex dynamical systems. In this paper, we will propose an extension of DMD that exploits low-rank tensor decompositions of potentially high-dimensional data sets to compute the corresponding DMD modes and eigenvalues. The goal is to reduce the computational complexity and also the amount of memory required to store the data in order to mitigate the curse of dimensionality. The efficiency of these tensor-based methods will be illustrated with the aid of several different fluid dynamics problems such as the von Kármán vortex street and the simulation of two merging vortices.

Item Type: | Article |
---|---|

Subjects: | Mathematical and Computer Sciences Mathematical and Computer Sciences > Mathematics |

Divisions: | Department of Mathematics and Computer Science > Institute of Mathematics Department of Mathematics and Computer Science > Institute of Mathematics > BioComputing Group |

ID Code: | 2306 |

Deposited By: | BioComp Admin |

Deposited On: | 14 Mar 2019 13:04 |

Last Modified: | 14 Mar 2019 13:04 |

Repository Staff Only: item control page