Comparison of manifold learning algorithms used in FSI data interpolation of curved surfaces

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書誌詳細
出版年:	Multidiscipline Modeling in Materials and Structures vol. 13, no. 2 (2017), p. 217-261
第一著者:	Liu, Ming-min
その他の著者:	Li, LZ, Zhang, Jun
出版事項:	Emerald Group Publishing Limited
主題:	Principal components analysis Partial differential equations Fluid-structure interaction Fault diagnosis Algorithms Interpolation Neural networks Planes Information processing Manifolds (mathematics) Data transmission Engineering Methods Machine learning Stress concentration Mathematical problems Learning algorithms Computer simulation
オンライン･アクセス:	Citation/Abstract Full Text Full Text - PDF
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その他の書誌記述
抄録:	Purpose The purpose of this paper is to discuss a data interpolation method of curved surfaces from the point of dimension reduction and manifold learning. Design/methodology/approach Instead of transmitting data of curved surfaces in 3D space directly, the method transmits data by unfolding 3D curved surfaces into 2D planes by manifold learning algorithms. The similarity between surface unfolding and manifold learning is discussed. Projection ability of several manifold learning algorithms is investigated to unfold curved surface. The algorithms' efficiency and their influences on the accuracy of data transmission are investigated by three examples. Findings It is found that the data interpolations using manifold learning algorithms LLE, HLLE and LTSA are efficient and accurate. Originality/value The method can improve the accuracies of coupling data interpolation and fluid-structure interaction simulation involving curved surfaces.
ISSN:	1573-6105 1573-6113
DOI:	10.1108/MMMS-07-2016-0032
ソース:	Materials Science Database