m (Move page script moved page Begiashvili et al 1970a to Begiashvili et al 2022a) |
|||
| (2 intermediate revisions by one other user not shown) | |||
| Line 6: | Line 6: | ||
== Abstract == | == Abstract == | ||
<pdf>Media:Draft_Sanchez Pinedo_343111130417_abstract.pdf</pdf> | <pdf>Media:Draft_Sanchez Pinedo_343111130417_abstract.pdf</pdf> | ||
| + | |||
| + | == Full Paper == | ||
| + | <pdf>Media:Draft_Sanchez Pinedo_343111130417_paper.pdf</pdf> | ||
Latest revision as of 16:06, 25 November 2022
Summary
Various modal decomposition techniques have been developed in the last decade [111]. We focus on data-driven approches, and since data flow volume is increasing day by day, it is important to study the performance of order reduction and feature detection algorithms. In this work we compare the performance and feature detection behaviour of energy and frequency based algorithms (Proper Orthogonal Decomposition [13] and Dynamic Mode Decomposition [46, 811]) on two data set testcases taken from fluid dynamics.
Abstract
Full Paper
Back to Top
GET PDF
Document information
Published on 24/11/22
Accepted on 24/11/22
Submitted on 24/11/22
Volume Computational Fluid Dynamics, 2022
DOI: 10.23967/eccomas.2022.117
Licence: CC BY-NC-SA license
Share this document
Keywords
claim authorship
Are you one of the authors of this document?