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Conference Paper: STORM: a nonlinear model order reduction method via symmetric tensor decomposition

TitleSTORM: a nonlinear model order reduction method via symmetric tensor decomposition
Authors
Issue Date2015
Citation
The 20th Asia and South Pacific Design Automation Conference (ASP-DAC 2015), Chiba/Tokyo, Japan, 19-22 January 2015. How to Cite?
AbstractNonlinear model order reduction has always been a challenging but important task in various science and engineering fields. In this paper, a novel symmetric tensor-based orderreduction method (STORM) is presented for simulating largescale nonlinear systems. The multidimensional data structure of symmetric tensors, as the higher order generalization of symmetric matrices, is utilized for the effective capture of highorder nonlinearities and efficient generation of compact models. Compared to the recent tensor-based nonlinear model order reduction (TNMOR) algorithm [1], STORM shows advantages in two aspects. First, STORM avoids the assumption of the existence of a low-rank tensor approximation. Second, with the use of the symmetric tensor decomposition, STORM allows significantly faster computation and less storage complexity than TNMOR. Numerical experiments demonstrate the superior computational efficiency and accuracy of STORM against existing nonlinear model order reduction methods.
Persistent Identifierhttp://hdl.handle.net/10722/216394

 

DC FieldValueLanguage
dc.contributor.authorDeng, J-
dc.contributor.authorLiu, H-
dc.contributor.authorBatselier, K-
dc.contributor.authorKwok, YK-
dc.contributor.authorWong, N-
dc.date.accessioned2015-09-18T05:26:16Z-
dc.date.available2015-09-18T05:26:16Z-
dc.date.issued2015-
dc.identifier.citationThe 20th Asia and South Pacific Design Automation Conference (ASP-DAC 2015), Chiba/Tokyo, Japan, 19-22 January 2015.-
dc.identifier.urihttp://hdl.handle.net/10722/216394-
dc.description.abstractNonlinear model order reduction has always been a challenging but important task in various science and engineering fields. In this paper, a novel symmetric tensor-based orderreduction method (STORM) is presented for simulating largescale nonlinear systems. The multidimensional data structure of symmetric tensors, as the higher order generalization of symmetric matrices, is utilized for the effective capture of highorder nonlinearities and efficient generation of compact models. Compared to the recent tensor-based nonlinear model order reduction (TNMOR) algorithm [1], STORM shows advantages in two aspects. First, STORM avoids the assumption of the existence of a low-rank tensor approximation. Second, with the use of the symmetric tensor decomposition, STORM allows significantly faster computation and less storage complexity than TNMOR. Numerical experiments demonstrate the superior computational efficiency and accuracy of STORM against existing nonlinear model order reduction methods.-
dc.languageeng-
dc.relation.ispartofAsia and South Pacific Design Automation Conference, ASP-DAC 2015-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.titleSTORM: a nonlinear model order reduction method via symmetric tensor decomposition-
dc.typeConference_Paper-
dc.identifier.emailLiu, H: htliu@eee.hku.hk-
dc.identifier.emailBatselier, K: kbatseli@hku.hk-
dc.identifier.emailKwok, YK: ykwok@hku.hk-
dc.identifier.emailWong, N: nwong@eee.hku.hk-
dc.identifier.authorityKwok, YK=rp00128-
dc.identifier.authorityWong, N=rp00190-
dc.description.naturepostprint-
dc.identifier.hkuros253246-
dc.customcontrol.immutablesml 151221-

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