An integral model for high‐accuracy and low‐accuracy experiments
A growing trend in engineering and science is to use multiple computer codes with different levels of accuracy to study the same complex system. Strategies are needed to combine the simulation results obtained at different levels of accuracy to produce an efficient surrogate model for prediction. In...
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| Veröffentlicht in: | Stat (International Statistical Institute) 2022-12, Vol.11 (1), p.n/a |
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| creator | Qi, Guanying Liu, Min‐Qian Yang, Jian‐Feng |
| description | A growing trend in engineering and science is to use multiple computer codes with different levels of accuracy to study the same complex system. Strategies are needed to combine the simulation results obtained at different levels of accuracy to produce an efficient surrogate model for prediction. In this paper, we propose an integral model to borrow as much information as possible from the low‐accuracy experiment. We ignore the Markov property assumed before and model the high‐accuracy experiment based on an integral form of the low‐accuracy experiment. The proposed model is more general thus better predictions are expected. Two explicit forms of some matrices and vectors used in our predictions are given. The effectiveness of the proposed model is illustrated with several examples. |
| doi_str_mv | 10.1002/sta4.531 |
| format | Article |
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Strategies are needed to combine the simulation results obtained at different levels of accuracy to produce an efficient surrogate model for prediction. In this paper, we propose an integral model to borrow as much information as possible from the low‐accuracy experiment. We ignore the Markov property assumed before and model the high‐accuracy experiment based on an integral form of the low‐accuracy experiment. The proposed model is more general thus better predictions are expected. Two explicit forms of some matrices and vectors used in our predictions are given. The effectiveness of the proposed model is illustrated with several examples.</description><subject>Complex systems</subject><subject>computer experiment</subject><subject>Gaussian process model</subject><subject>Kriging</subject><subject>Mathematical analysis</subject><subject>Model accuracy</subject><subject>nested space‐filling design</subject><issn>2049-1573</issn><issn>2049-1573</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><recordid>eNp1kE1qwzAQRkVpoSEN9AiGbrpxqpFkS1qa9BcCXTRdC9mWEgfHdiWb1LseoWfsSWrjLrLpaoaPxzfDQ-ga8BIwJne-1WwZUThDM4KZDCHi9Pxkv0QL7_cYY4iIpDGdofukCoqqNVuny-BQ56YMbO2CXbHd_Xx96yzrnM76QFd5UNbH08h8NsYVB1O1_gpdWF16s_ibc_T--LBZPYfr16eXVbIOMxAMwpwQGsW5tIxKplORxtxGBiDFQgDhWlpptWBMWGMh10MAXHAhUgk55cTQObqZehtXf3TGt2pfd64aTioiJKaCUy4H6naiMld774xVzfCndr0CrEZNatSkBk0DGk7osShN_y-n3jYJG_lfksNpjQ</recordid><startdate>202212</startdate><enddate>202212</enddate><creator>Qi, Guanying</creator><creator>Liu, Min‐Qian</creator><creator>Yang, Jian‐Feng</creator><general>Wiley Subscription Services, Inc</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><orcidid>https://orcid.org/0000-0002-2271-4798</orcidid></search><sort><creationdate>202212</creationdate><title>An integral model for high‐accuracy and low‐accuracy experiments</title><author>Qi, Guanying ; Liu, Min‐Qian ; Yang, Jian‐Feng</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c1841-d22356d9f4394ab8b67f5e11b088127a9f9fa8448fef1da7a9178788b91d372e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Complex systems</topic><topic>computer experiment</topic><topic>Gaussian process model</topic><topic>Kriging</topic><topic>Mathematical analysis</topic><topic>Model accuracy</topic><topic>nested space‐filling design</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Qi, Guanying</creatorcontrib><creatorcontrib>Liu, Min‐Qian</creatorcontrib><creatorcontrib>Yang, Jian‐Feng</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Stat (International Statistical Institute)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Qi, Guanying</au><au>Liu, Min‐Qian</au><au>Yang, Jian‐Feng</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>An integral model for high‐accuracy and low‐accuracy experiments</atitle><jtitle>Stat (International Statistical Institute)</jtitle><date>2022-12</date><risdate>2022</risdate><volume>11</volume><issue>1</issue><epage>n/a</epage><issn>2049-1573</issn><eissn>2049-1573</eissn><abstract>A growing trend in engineering and science is to use multiple computer codes with different levels of accuracy to study the same complex system. Strategies are needed to combine the simulation results obtained at different levels of accuracy to produce an efficient surrogate model for prediction. In this paper, we propose an integral model to borrow as much information as possible from the low‐accuracy experiment. We ignore the Markov property assumed before and model the high‐accuracy experiment based on an integral form of the low‐accuracy experiment. The proposed model is more general thus better predictions are expected. Two explicit forms of some matrices and vectors used in our predictions are given. The effectiveness of the proposed model is illustrated with several examples.</abstract><cop>The Hague</cop><pub>Wiley Subscription Services, Inc</pub><doi>10.1002/sta4.531</doi><tpages>14</tpages><orcidid>https://orcid.org/0000-0002-2271-4798</orcidid></addata></record> |
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| language | eng |
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| source | Wiley Online Library - AutoHoldings Journals |
| subjects | Complex systems computer experiment Gaussian process model Kriging Mathematical analysis Model accuracy nested space‐filling design |
| title | An integral model for high‐accuracy and low‐accuracy experiments |
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