| dc.contributor.author | Ollar, J | en_US |
| dc.contributor.author | Mortished, C | en_US |
| dc.contributor.author | Jones, R | en_US |
| dc.contributor.author | Sienz, J | en_US |
| dc.contributor.author | Toropov, V | en_US |
| dc.date.accessioned | 2017-01-24T15:05:57Z | |
| dc.date.available | 2016-11-17 | en_US |
| dc.date.issued | 2017-06 | en_US |
| dc.date.submitted | 2017-01-20T18:18:05.707Z | |
| dc.identifier.issn | 1615-147X | en_US |
| dc.identifier.uri | http://qmro.qmul.ac.uk/xmlui/handle/123456789/18948 | |
| dc.description.sponsorship | The authors are very grateful to the Altair HyperStudy team for their support throughout this work. Jonathan Ollar, Vassili Toropov and Royston Jones greatly appreciate funding from European Union Seventh Framework Programme FP7-PEOPLE-2012-ITN under grant agreement 316394, Aerospace Multidisciplinarity Enabling DEsign Optimization (AMEDEO)Marie Curie Initial Training Network. Charles Mortished and Johann Sienz greatly appreciate funding from EPSRC EDT (EP/I015507/1) Manufacturing Advances Through Training Engineering Researchers (MATTER). Vassili Toropov is grateful for the support provided by the Russian Science Foundation, project No. 16-11-10150. | en_US |
| dc.format.extent | 2029 - 2044 | en_US |
| dc.relation.ispartof | STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION | en_US |
| dc.rights | This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. | |
| dc.subject | Hyper-parameter optimisation | en_US |
| dc.subject | SQP | en_US |
| dc.subject | Adjoint method | en_US |
| dc.subject | Condition number | en_US |
| dc.subject | Kriging | en_US |
| dc.subject | Gradient-enhanced kriging | en_US |
| dc.subject | Gaussian processes | en_US |
| dc.title | Gradient based hyper-parameter optimisation for well conditioned kriging metamodels | en_US |
| dc.type | Article | |
| dc.rights.holder | © The Author(s) 2016 | |
| dc.identifier.doi | 10.1007/s00158-016-1626-8 | en_US |
| pubs.author-url | http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000400601200006&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=612ae0d773dcbdba3046f6df545e9f6a | en_US |
| pubs.issue | 6 | en_US |
| pubs.notes | Not known | en_US |
| pubs.notes | Open access publication | en_US |
| pubs.publication-status | Published | en_US |
| pubs.volume | 55 | en_US |
