Preconditioned dynamic mode decomposition and mode selection algorithms for large datasets using incremental proper orthogonal decomposition

Preconditioned dynamic mode decomposition and mode selection algorithms for large datasets using incremental proper orthogonal decomposition

In this letter, we propose a simple and efficient framework of dynamic mode decomposition (DMD) and mode selection for large datasets. The proposed framework explicitly introduces a preconditioning step using an incremental proper orthogonal decomposition (POD) to DMD and mode selection algorithms....

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Bibliographic Details
Main Author: Yuya Ohmichi
Format: Article
Language:English
Published: AIP Publishing LLC 2017-07-01
Series:AIP Advances
Online Access:http://dx.doi.org/10.1063/1.4996024
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spelling doaj-ee47fe34164b4107938e4f640881bdf52020-11-25T02:48:42ZengAIP Publishing LLCAIP Advances2158-32262017-07-0177075318075318-910.1063/1.4996024083707ADVPreconditioned dynamic mode decomposition and mode selection algorithms for large datasets using incremental proper orthogonal decompositionYuya Ohmichi0Aeronautical Technology Directorate, Japan Aerospace Exploration Agency, 7-44-1 Jindaijihigashi, Chofu, Tokyo 182-8522, JapanIn this letter, we propose a simple and efficient framework of dynamic mode decomposition (DMD) and mode selection for large datasets. The proposed framework explicitly introduces a preconditioning step using an incremental proper orthogonal decomposition (POD) to DMD and mode selection algorithms. By performing the preconditioning step, the DMD and mode selection can be performed with low memory consumption and therefore can be applied to large datasets. Additionally, we propose a simple mode selection algorithm based on a greedy method. The proposed framework is applied to the analysis of three-dimensional flow around a circular cylinder.http://dx.doi.org/10.1063/1.4996024
collection DOAJ
language English
format Article
sources DOAJ
author Yuya Ohmichi
spellingShingle Yuya Ohmichi
Preconditioned dynamic mode decomposition and mode selection algorithms for large datasets using incremental proper orthogonal decomposition
AIP Advances
author_facet Yuya Ohmichi
author_sort Yuya Ohmichi
title Preconditioned dynamic mode decomposition and mode selection algorithms for large datasets using incremental proper orthogonal decomposition
title_short Preconditioned dynamic mode decomposition and mode selection algorithms for large datasets using incremental proper orthogonal decomposition
title_full Preconditioned dynamic mode decomposition and mode selection algorithms for large datasets using incremental proper orthogonal decomposition
title_fullStr Preconditioned dynamic mode decomposition and mode selection algorithms for large datasets using incremental proper orthogonal decomposition
title_full_unstemmed Preconditioned dynamic mode decomposition and mode selection algorithms for large datasets using incremental proper orthogonal decomposition
title_sort preconditioned dynamic mode decomposition and mode selection algorithms for large datasets using incremental proper orthogonal decomposition
publisher AIP Publishing LLC
series AIP Advances
issn 2158-3226
publishDate 2017-07-01
description In this letter, we propose a simple and efficient framework of dynamic mode decomposition (DMD) and mode selection for large datasets. The proposed framework explicitly introduces a preconditioning step using an incremental proper orthogonal decomposition (POD) to DMD and mode selection algorithms. By performing the preconditioning step, the DMD and mode selection can be performed with low memory consumption and therefore can be applied to large datasets. Additionally, we propose a simple mode selection algorithm based on a greedy method. The proposed framework is applied to the analysis of three-dimensional flow around a circular cylinder.
url http://dx.doi.org/10.1063/1.4996024
work_keys_str_mv AT yuyaohmichi preconditioneddynamicmodedecompositionandmodeselectionalgorithmsforlargedatasetsusingincrementalproperorthogonaldecomposition
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