Klus, S. and Gelß, P. and Peitz, S. and Schütte, Ch.
(2016)
*Tensor-based dynamic mode decomposition.*
SIAM Journal on Scientific Computing
.
ISSN ISSN 1064-8275 (print); 1095-7197 (electronic)
(Submitted)

Full text not available from this repository.

Official URL: http://arxiv.org/abs/1606.06625

## 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 K\'arm\'an vortex street and the simulation of two merging vortices.

Item Type: | Article |
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Subjects: | Mathematical and Computer Sciences > Mathematics > Applied Mathematics |

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

ID Code: | 1903 |

Deposited By: | Ulrike Eickers |

Deposited On: | 14 Jul 2016 13:28 |

Last Modified: | 20 Jan 2017 10:00 |

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