von Larcher, T. and Klein, R.
(2017)
*On identification of self-similar characteristics using the Tensor Train decomposition method with application to channel turbulence flow.*
SFB 1114 Preprint in arXiv:1708.07780
.
pp. 1-15.

Full text not available from this repository.

Official URL: https://arxiv.org/abs/1708.07780

## Abstract

A study on the application of the Tensor Train decomposition method to 3D direct numerical simulation data of channel turbulence flow is presented. The approach is validated with respect to compression rate and storage requirement. In tests with synthetic data, it is found that grid-aligned self-similar patterns are well captured, and also the application to non grid-aligned self-similarity yields satisfying results. It is observed that the shape of the input Tensor significantly affects the compression rate. Applied to data of channel turbulent flow, the Tensor Train format allows for surprisingly high compression rates whilst ensuring low relative errors.

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

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

Divisions: | Department of Mathematics and Computer Science > Institute of Mathematics > Geophysical Fluid Dynamics Group |

ID Code: | 2100 |

Deposited By: | Silvia Hoemke |

Deposited On: | 28 Aug 2017 10:23 |

Last Modified: | 30 Aug 2017 13:02 |

Repository Staff Only: item control page