Simulation metamodels for modeling output distribution parameters
Metamodels are functions with calibrated parameters, used as abstractions and simplifications of the simulation model. A metamodel exposes the system's input-output relationship and can be used as an analysis tool for solving optimization problems or as a surrogate for building blocks in larger...
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creator | Santos, I.R. Santos, P.R. |
description | Metamodels are functions with calibrated parameters, used as abstractions and simplifications of the simulation model. A metamodel exposes the system's input-output relationship and can be used as an analysis tool for solving optimization problems or as a surrogate for building blocks in larger scale simulations. Our approach is to analyze statistically the response by modeling the normal distribution mean and variance functions, in order to better depict the problem and improve the knowledge about the system. The metamodel is checked using the confidence intervals of the estimated distribution parameters, and new design points are employed for predictive validation. An example is used to illustrate the development of analysis and surrogate metamodels. |
doi_str_mv | 10.1109/WSC.2007.4419687 |
format | Conference Proceeding |
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An example is used to illustrate the development of analysis and surrogate metamodels.</description><subject>Analysis of variance</subject><subject>Analytical models</subject><subject>Computational modeling</subject><subject>Design for experiments</subject><subject>Gaussian distribution</subject><subject>Informatics</subject><subject>Mathematical model</subject><subject>Mathematics</subject><subject>Parameter estimation</subject><subject>Uncertainty</subject><issn>0891-7736</issn><issn>1558-4305</issn><isbn>9781424413058</isbn><isbn>1424413052</isbn><isbn>9781424413065</isbn><isbn>1424413060</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2007</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNpVkD1PwzAYhM2XRCjdkVjyBxLe198Zq4ovqRJDQYyVHdvIKGkiOxn491SlC9Pd6R7dcITcIdSI0Dx8btc1BVA159hIrc7IslEaOT1kBlKckwKF0BVnIC7-dUJfkgJ0g5VSTF6Tm5y_AVALpAVZbWM_d2aKw77s_WT6wfkul2FI5dHG_Vc5zNM4T6WLeUrRzkd2NMkceJ_yLbkKpst-edIF-Xh6fF-_VJu359f1alNFZFRVTgTKWsFZaBRK2zprwUrKPGjLRItWujY4qjxVRiLjXgofnBSOGksRJFuQ-7_d6L3fjSn2Jv3sTm-wX_eKT7g</recordid><startdate>200712</startdate><enddate>200712</enddate><creator>Santos, I.R.</creator><creator>Santos, P.R.</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>200712</creationdate><title>Simulation metamodels for modeling output distribution parameters</title><author>Santos, I.R. ; Santos, P.R.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i1327-d5f23c543f9716bcdbb0b623e08b35c1b6dcfd27e27a6134e65efd65d2ab21063</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2007</creationdate><topic>Analysis of variance</topic><topic>Analytical models</topic><topic>Computational modeling</topic><topic>Design for experiments</topic><topic>Gaussian distribution</topic><topic>Informatics</topic><topic>Mathematical model</topic><topic>Mathematics</topic><topic>Parameter estimation</topic><topic>Uncertainty</topic><toplevel>online_resources</toplevel><creatorcontrib>Santos, I.R.</creatorcontrib><creatorcontrib>Santos, P.R.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Santos, I.R.</au><au>Santos, P.R.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Simulation metamodels for modeling output distribution parameters</atitle><btitle>2007 Winter Simulation Conference</btitle><stitle>WSC</stitle><date>2007-12</date><risdate>2007</risdate><spage>910</spage><epage>918</epage><pages>910-918</pages><issn>0891-7736</issn><eissn>1558-4305</eissn><isbn>9781424413058</isbn><isbn>1424413052</isbn><eisbn>9781424413065</eisbn><eisbn>1424413060</eisbn><abstract>Metamodels are functions with calibrated parameters, used as abstractions and simplifications of the simulation model. A metamodel exposes the system's input-output relationship and can be used as an analysis tool for solving optimization problems or as a surrogate for building blocks in larger scale simulations. Our approach is to analyze statistically the response by modeling the normal distribution mean and variance functions, in order to better depict the problem and improve the knowledge about the system. The metamodel is checked using the confidence intervals of the estimated distribution parameters, and new design points are employed for predictive validation. An example is used to illustrate the development of analysis and surrogate metamodels.</abstract><pub>IEEE</pub><doi>10.1109/WSC.2007.4419687</doi><tpages>9</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Analysis of variance Analytical models Computational modeling Design for experiments Gaussian distribution Informatics Mathematical model Mathematics Parameter estimation Uncertainty |
title | Simulation metamodels for modeling output distribution parameters |
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