Incremental modeling of a new high-order polynomial surrogate model

Wu, J; Luo, Z; Zheng, J; Jiang, C

Incremental modeling of a new high-order polynomial surrogate model

Wu, J

Luo, Z

Zheng, J Jiang, C

Permalink

Publication Type:: Journal Article
Citation:: Applied Mathematical Modelling, 2016, 40 (7-8), pp. 4681 - 4699
Issue Date:: 2016-04-01

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download Accepted Manuscript VersionAdobe PDF (1.94 MB)

View on publisher's site

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Wu, J https://orcid.org/0000-0002-4925-4242	en_US
dc.contributor.author	Luo, Z https://orcid.org/0000-0002-6953-3986	en_US
dc.contributor.author	Zheng, J	en_US
dc.contributor.author	Jiang, C	en_US
dc.date.available	2020-05-25T19:03:19Z
dc.date.issued	2016-04-01	en_US
dc.identifier.citation	Applied Mathematical Modelling, 2016, 40 (7-8), pp. 4681 - 4699	en_US
dc.identifier.issn	0307-904X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/43407
dc.description.abstract	© 2015. This study will develop a new high-order polynomial surrogate model (HOPSM) to overcome routines of expensive computer simulations in engineering. The proposed HOPSM is expected to keep advantages of the traditional low-order polynomial models in efficiency, transparency and simplicity, while avoid their disadvantage in accuracy. The zeros of Chebyshev polynomials having the highest allowable order will be utilized as the sampling candidates to improve stability and accuracy of the approximation. In the numerical process, a space-filling scheme is used to generate the initial set of samples, and then an incremental method based on the maximin principle is established to select more samples from all candidates. At the same time, the order of HOPSM is sequentially updated by using an order incremental scheme, to adaptively increase the polynomial order along with the increase of the sample size. After the order increment, the polynomial with the largest adjusted R-square is determined as the final HOPSM. Several typical test functions and two engineering applications are used to demonstrate the effectiveness of the proposed surrogate modeling method.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP150102751
dc.relation	http://purl.org/au-research/grants/arc/DP160102491
dc.relation.ispartof	Applied Mathematical Modelling	en_US
dc.relation.isbasedon	10.1016/j.apm.2015.12.002	en_US
dc.subject.classification	Mechanical Engineering & Transports	en_US
dc.title	Incremental modeling of a new high-order polynomial surrogate model	en_US
dc.type	Journal Article
utslib.citation.volume	7-8	en_US
utslib.citation.volume	40	en_US
utslib.for	091307 Numerical Modelling and Mechanical Characterisation	en_US
utslib.for	0102 Applied Mathematics	en_US
utslib.for	0103 Numerical and Computational Mathematics	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
utslib.copyright.status	open_access
pubs.issue	7-8	en_US
pubs.publication-status	Published	en_US
pubs.volume	40	en_US

Abstract:

© 2015. This study will develop a new high-order polynomial surrogate model (HOPSM) to overcome routines of expensive computer simulations in engineering. The proposed HOPSM is expected to keep advantages of the traditional low-order polynomial models in efficiency, transparency and simplicity, while avoid their disadvantage in accuracy. The zeros of Chebyshev polynomials having the highest allowable order will be utilized as the sampling candidates to improve stability and accuracy of the approximation. In the numerical process, a space-filling scheme is used to generate the initial set of samples, and then an incremental method based on the maximin principle is established to select more samples from all candidates. At the same time, the order of HOPSM is sequentially updated by using an order incremental scheme, to adaptively increase the polynomial order along with the increase of the sample size. After the order increment, the polynomial with the largest adjusted R-square is determined as the final HOPSM. Several typical test functions and two engineering applications are used to demonstrate the effectiveness of the proposed surrogate modeling method.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/43407