{"id":"https://openalex.org/W3007474926","doi":"https://doi.org/10.1080/03610918.2020.1728317","title":"Iterative method for tuning complex simulation code","display_name":"Iterative method for tuning complex simulation code","publication_year":2020,"publication_date":"2020-02-20","ids":{"openalex":"https://openalex.org/W3007474926","doi":"https://doi.org/10.1080/03610918.2020.1728317","mag":"3007474926"},"language":"en","primary_location":{"id":"doi:10.1080/03610918.2020.1728317","is_oa":false,"landing_page_url":"https://doi.org/10.1080/03610918.2020.1728317","pdf_url":null,"source":{"id":"https://openalex.org/S153329750","display_name":"Communications in Statistics - Simulation and Computation","issn_l":"0361-0918","issn":["0361-0918","1532-4141"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Communications in Statistics - Simulation and Computation","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2007.09865","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Yun Am Seo","orcid":null},"institutions":[{"id":"https://openalex.org/I4210155906","display_name":"National Institute of Meteorological Sciences","ror":"https://ror.org/04m2hj141","country_code":"KR","type":"facility","lineage":["https://openalex.org/I4210155906"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Yun Am Seo","raw_affiliation_strings":["AI Weather Forecast Research Team, National Institute of Meteorological Science (NIMS), Seogwipo, Korea"],"affiliations":[{"raw_affiliation_string":"AI Weather Forecast Research Team, National Institute of Meteorological Science (NIMS), Seogwipo, Korea","institution_ids":["https://openalex.org/I4210155906"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Youngsaeng Lee","orcid":null},"institutions":[{"id":"https://openalex.org/I198972184","display_name":"Korea Electric Power Corporation (South Korea)","ror":"https://ror.org/04fperw70","country_code":"KR","type":"company","lineage":["https://openalex.org/I198972184"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Youngsaeng Lee","raw_affiliation_strings":["Digital Transformation Department, Korea Electric Power Corporation, Seoul, Korea"],"affiliations":[{"raw_affiliation_string":"Digital Transformation Department, Korea Electric Power Corporation, Seoul, Korea","institution_ids":["https://openalex.org/I198972184"]}]},{"author_position":"last","author":{"id":null,"display_name":"Jeong-Soo Park","orcid":"https://orcid.org/0000-0002-8460-4869"},"institutions":[{"id":"https://openalex.org/I111277659","display_name":"Chonnam National University","ror":"https://ror.org/05kzjxq56","country_code":"KR","type":"education","lineage":["https://openalex.org/I111277659"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Jeong-Soo Park","raw_affiliation_strings":["Department of Statistics, Chonnam National University, Gwangju, Korea"],"affiliations":[{"raw_affiliation_string":"Department of Statistics, Chonnam National University, Gwangju, Korea","institution_ids":["https://openalex.org/I111277659"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I111277659"],"apc_list":null,"apc_paid":null,"fwci":0.289,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.60695525,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":"51","issue":"7","first_page":"3975","last_page":"3992"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10848","display_name":"Advanced Multi-Objective Optimization Algorithms","score":0.26170000433921814,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/T10848","display_name":"Advanced Multi-Objective Optimization Algorithms","score":0.26170000433921814,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.15399999916553497,"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/T11195","display_name":"Simulation Techniques and Applications","score":0.10840000212192535,"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":0.7835999727249146},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.6284000277519226},{"id":"https://openalex.org/keywords/iterative-and-incremental-development","display_name":"Iterative and incremental development","score":0.5389000177383423},{"id":"https://openalex.org/keywords/iterative-method","display_name":"Iterative method","score":0.505299985408783},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5019999742507935},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.47769999504089355},{"id":"https://openalex.org/keywords/iterative-refinement","display_name":"Iterative refinement","score":0.44359999895095825},{"id":"https://openalex.org/keywords/least-squares-function-approximation","display_name":"Least-squares function approximation","score":0.4230000078678131}],"concepts":[{"id":"https://openalex.org/C86610423","wikidata":"https://www.wikidata.org/wiki/Q1925081","display_name":"Metamodeling","level":2,"score":0.7835999727249146},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.670199990272522},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.6284000277519226},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.6140999794006348},{"id":"https://openalex.org/C143587482","wikidata":"https://www.wikidata.org/wiki/Q1543216","display_name":"Iterative and incremental development","level":2,"score":0.5389000177383423},{"id":"https://openalex.org/C159694833","wikidata":"https://www.wikidata.org/wiki/Q2321565","display_name":"Iterative method","level":2,"score":0.505299985408783},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5019999742507935},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.47769999504089355},{"id":"https://openalex.org/C2779982483","wikidata":"https://www.wikidata.org/wiki/Q6094420","display_name":"Iterative refinement","level":2,"score":0.44359999895095825},{"id":"https://openalex.org/C9936470","wikidata":"https://www.wikidata.org/wiki/Q6510405","display_name":"Least-squares function approximation","level":3,"score":0.4230000078678131},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.41019999980926514},{"id":"https://openalex.org/C207467116","wikidata":"https://www.wikidata.org/wiki/Q4385666","display_name":"Inverse","level":2,"score":0.40700000524520874},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.39809998869895935},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.34470000863075256},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.3212999999523163},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.30320000648498535},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.29910001158714294},{"id":"https://openalex.org/C87466663","wikidata":"https://www.wikidata.org/wiki/Q5157537","display_name":"Computer experiment","level":2,"score":0.27300000190734863},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.2685000002384186},{"id":"https://openalex.org/C45923927","wikidata":"https://www.wikidata.org/wiki/Q3319230","display_name":"Non-linear least squares","level":3,"score":0.2621999979019165},{"id":"https://openalex.org/C55037315","wikidata":"https://www.wikidata.org/wiki/Q5421151","display_name":"Experimental data","level":2,"score":0.25780001282691956},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2502000033855438}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1080/03610918.2020.1728317","is_oa":false,"landing_page_url":"https://doi.org/10.1080/03610918.2020.1728317","pdf_url":null,"source":{"id":"https://openalex.org/S153329750","display_name":"Communications in Statistics - Simulation and Computation","issn_l":"0361-0918","issn":["0361-0918","1532-4141"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Communications in Statistics - Simulation and Computation","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:2007.09865","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2007.09865","pdf_url":"https://arxiv.org/pdf/2007.09865","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2007.09865","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2007.09865","pdf_url":"https://arxiv.org/pdf/2007.09865","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G5205999697","display_name":null,"funder_award_id":"518","funder_id":"https://openalex.org/F4320322120","funder_display_name":"National Research Foundation of Korea"}],"funders":[{"id":"https://openalex.org/F4320322120","display_name":"National Research Foundation of Korea","ror":"https://ror.org/013aysd81"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W113733777","https://openalex.org/W1967910251","https://openalex.org/W1970928027","https://openalex.org/W1973333099","https://openalex.org/W1973410308","https://openalex.org/W1977631358","https://openalex.org/W1991788899","https://openalex.org/W1992870395","https://openalex.org/W2036008353","https://openalex.org/W2043170151","https://openalex.org/W2051979767","https://openalex.org/W2067278522","https://openalex.org/W2072701775","https://openalex.org/W2092612581","https://openalex.org/W2094129114","https://openalex.org/W2119142031","https://openalex.org/W2119902028","https://openalex.org/W2137390208","https://openalex.org/W2158519240","https://openalex.org/W2278053714","https://openalex.org/W2339145210","https://openalex.org/W2497151737","https://openalex.org/W2514303448","https://openalex.org/W2567289819","https://openalex.org/W2742425491","https://openalex.org/W2962851339","https://openalex.org/W4237236652","https://openalex.org/W4249753629"],"related_works":[],"abstract_inverted_index":{"Tuning":[0],"a":[1,14,20,56,165],"complex":[2],"simulation":[3,106,168],"code":[4,15,107,142,169],"refers":[5],"to":[6,19,35,164],"the":[7,11,30,36,47,71,76,99,102,105,121,148,154,158],"process":[8,33,58],"of":[9,13,22,38,70,104],"improving":[10],"agreement":[12],"calculation":[16],"with":[17,143],"respect":[18],"set":[21],"experimental":[23,130],"data":[24,131],"by":[25,64,116],"adjusting":[26],"parameters":[27,103],"implemented":[28],"in":[29,95],"code.":[31],"This":[32,124],"belongs":[34],"class":[37],"inverse":[39],"problems":[40],"or":[41],"model":[42],"calibration.":[43],"For":[44],"this":[45,88,96],"problem,":[46],"approximated":[48],"nonlinear":[49],"least":[50],"squares":[51],"(ANLS)":[52],"method":[53,73,156],"based":[54],"on":[55],"Gaussian":[57],"(GP)":[59],"metamodel":[60,77,110],"has":[61],"been":[62],"employed":[63],"some":[65],"researchers.":[66],"A":[67,135],"potential":[68],"drawback":[69],"ANLS":[72,122,155],"is":[74,78,170],"that":[75,147],"built":[79],"only":[80],"once":[81],"and":[82,108,114,120,129,157],"not":[83],"updated":[84,115],"thereafter.":[85],"To":[86],"address":[87],"difficulty,":[89],"we":[90],"propose":[91],"an":[92,162],"iterative":[93],"algorithm":[94,125,150],"study.":[97],"In":[98],"proposed":[100,149],"algorithm,":[101],"GP":[109],"are":[111],"alternatively":[112],"re-estimated":[113],"maximum":[117],"likelihood":[118],"estimation":[119],"method.":[123],"uses":[126],"both":[127],"computer":[128,141],"repeatedly":[132],"until":[133],"convergence.":[134],"study":[136],"using":[137],"toy-models":[138],"including":[139],"inexact":[140],"bias":[144],"terms":[145],"reveals":[146],"performs":[151],"better":[152],"than":[153],"conditional-likelihood-based":[159],"approach.":[160],"Finally,":[161],"application":[163],"nuclear":[166],"fusion":[167],"illustrated.":[171]},"counts_by_year":[{"year":2020,"cited_by_count":2}],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2020-03-06T00:00:00"}