{"id":"https://openalex.org/W4238134598","doi":"https://doi.org/10.1109/wsc.2012.6464998","title":"Allocation of simulation effort for neural network vs. regression metamodels","display_name":"Allocation of simulation effort for neural network vs. regression metamodels","publication_year":2012,"publication_date":"2012-12-01","ids":{"openalex":"https://openalex.org/W4238134598","doi":"https://doi.org/10.1109/wsc.2012.6464998"},"language":"en","primary_location":{"id":"doi:10.1109/wsc.2012.6464998","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wsc.2012.6464998","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings Title: Proceedings of the 2012 Winter Simulation Conference (WSC)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5073243681","display_name":"Corinne MacDonald","orcid":null},"institutions":[{"id":"https://openalex.org/I129902397","display_name":"Dalhousie University","ror":"https://ror.org/01e6qks80","country_code":"CA","type":"education","lineage":["https://openalex.org/I129902397"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Corinne MacDonald","raw_affiliation_strings":["Dalhousie University, Halifax, NS, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dalhousie University, Halifax, NS, Canada","institution_ids":["https://openalex.org/I129902397"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5084837843","display_name":"Eldon A. Gunn","orcid":null},"institutions":[{"id":"https://openalex.org/I129902397","display_name":"Dalhousie University","ror":"https://ror.org/01e6qks80","country_code":"CA","type":"education","lineage":["https://openalex.org/I129902397"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Eldon A. Gunn","raw_affiliation_strings":["Dalhousie University, Halifax, NS, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dalhousie University, Halifax, NS, Canada","institution_ids":["https://openalex.org/I129902397"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I129902397"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.32975299,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"3","issue":null,"first_page":"1","last_page":"12"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9980000257492065,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9980000257492065,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11206","display_name":"Model Reduction and Neural Networks","score":0.9941999912261963,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11195","display_name":"Simulation Techniques and Applications","score":0.9807000160217285,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/metamodeling","display_name":"Metamodeling","score":0.8167349100112915},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7204800248146057},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6987987756729126},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.5336951017379761},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.4897735118865967},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.4400036931037903},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.41587895154953003},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3375222682952881},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.33106061816215515},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.1587488055229187},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.14063423871994019}],"concepts":[{"id":"https://openalex.org/C86610423","wikidata":"https://www.wikidata.org/wiki/Q1925081","display_name":"Metamodeling","level":2,"score":0.8167349100112915},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7204800248146057},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6987987756729126},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.5336951017379761},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.4897735118865967},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.4400036931037903},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.41587895154953003},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3375222682952881},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.33106061816215515},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.1587488055229187},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.14063423871994019},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/wsc.2012.6464998","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wsc.2012.6464998","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings Title: Proceedings of the 2012 Winter Simulation Conference (WSC)","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.416.5050","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.416.5050","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.informs-sim.org/wsc12papers/includes/files/con165.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W1545877870","https://openalex.org/W1981494355","https://openalex.org/W1992870153","https://openalex.org/W2017729969","https://openalex.org/W2019787440","https://openalex.org/W2033753530","https://openalex.org/W2035345434","https://openalex.org/W2043193190","https://openalex.org/W2051004828","https://openalex.org/W2076118331","https://openalex.org/W2138747680","https://openalex.org/W2148049885","https://openalex.org/W2148080122","https://openalex.org/W2149238237","https://openalex.org/W2155482699","https://openalex.org/W3124502797","https://openalex.org/W3141954048","https://openalex.org/W3152035376","https://openalex.org/W4254114241"],"related_works":["https://openalex.org/W4289356671","https://openalex.org/W2389155397","https://openalex.org/W2165884543","https://openalex.org/W2312753042","https://openalex.org/W3186837933","https://openalex.org/W2368989808","https://openalex.org/W2034959125","https://openalex.org/W2355687852","https://openalex.org/W2621086889","https://openalex.org/W3174513558"],"abstract_inverted_index":{"The":[0],"construction":[1],"of":[2,11,20,42,67,101,111],"a":[3,64,72,98],"neural":[4,135],"network":[5,136],"simulation":[6,27,36,50,68,95],"metamodel":[7],"requires":[8],"the":[9,18,21,26,43,57,60,82,108,112],"generation":[10],"training":[12],"data;":[13],"design":[14],"points":[15],"(inputs)":[16],"and":[17,55,71],"estimate":[19],"corresponding":[22],"output":[23,45],"generated":[24],"by":[25,47],"model.":[28],"A":[29],"common":[30],"methodology":[31],"is":[32,90],"to":[33,91],"focus":[34],"some":[35],"effort":[37,69,96],"in":[38,133],"obtaining":[39],"accurate":[40],"estimates":[41,110],"expected":[44,84,113],"values":[46],"executing":[48],"several":[49,126],"replications":[51],"at":[52,115],"each":[53,116],"point":[54,117],"taking":[56],"average":[58],"as":[59],"estimate.":[61],"However,":[62],"with":[63],"limited":[65],"amount":[66],"available":[70],"rather":[73],"large":[74],"input":[75,102],"space,":[76],"this":[77,129,138],"approach":[78,89,130],"may":[79,131],"not":[80],"produce":[81],"best":[83],"value":[85],"approximations.":[86],"An":[87],"alternate":[88],"distribute":[92],"that":[93,128],"same":[94],"over":[97],"larger":[99],"sample":[100],"points,":[103],"even":[104],"if":[105],"it":[106],"means":[107],"resulting":[109],"outputs":[114],"will":[118,123],"be":[119],"less":[120],"accurate.":[121],"We":[122],"show":[124],"through":[125],"examples":[127],"result":[132],"better":[134],"metamodels;":[137],"conclusion":[139],"differs":[140],"from":[141],"other":[142],"studies":[143],"involving":[144],"regression":[145],"metamodels.":[146]},"counts_by_year":[{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
