{"id":"https://openalex.org/W2790101447","doi":"https://doi.org/10.3390/e20030201","title":"Global Optimization Employing Gaussian Process-Based Bayesian Surrogates","display_name":"Global Optimization Employing Gaussian Process-Based Bayesian Surrogates","publication_year":2018,"publication_date":"2018-03-16","ids":{"openalex":"https://openalex.org/W2790101447","doi":"https://doi.org/10.3390/e20030201","mag":"2790101447","pmid":"https://pubmed.ncbi.nlm.nih.gov/33265292"},"language":"en","primary_location":{"id":"doi:10.3390/e20030201","is_oa":true,"landing_page_url":"https://doi.org/10.3390/e20030201","pdf_url":"https://www.mdpi.com/1099-4300/20/3/201/pdf?version=1521209663","source":{"id":"https://openalex.org/S195231649","display_name":"Entropy","issn_l":"1099-4300","issn":["1099-4300"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Entropy","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/1099-4300/20/3/201/pdf?version=1521209663","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5053370786","display_name":"R. Preuss","orcid":"https://orcid.org/0009-0004-9725-2948"},"institutions":[{"id":"https://openalex.org/I4210139716","display_name":"Max Planck Institute for Plasma Physics","ror":"https://ror.org/03taest98","country_code":"DE","type":"facility","lineage":["https://openalex.org/I149899117","https://openalex.org/I4210139716"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Roland Preuss","raw_affiliation_strings":["Max-Planck-Institute for Plasma Physics, EURATOM Association, 85748 Garching, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Max-Planck-Institute for Plasma Physics, EURATOM Association, 85748 Garching, Germany","institution_ids":["https://openalex.org/I4210139716"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5024026339","display_name":"U. von Toussaint","orcid":"https://orcid.org/0000-0002-8867-1014"},"institutions":[{"id":"https://openalex.org/I4210139716","display_name":"Max Planck Institute for Plasma Physics","ror":"https://ror.org/03taest98","country_code":"DE","type":"facility","lineage":["https://openalex.org/I149899117","https://openalex.org/I4210139716"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Udo Von Toussaint","raw_affiliation_strings":["Max-Planck-Institute for Plasma Physics, EURATOM Association, 85748 Garching, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Max-Planck-Institute for Plasma Physics, EURATOM Association, 85748 Garching, Germany","institution_ids":["https://openalex.org/I4210139716"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5053370786"],"corresponding_institution_ids":["https://openalex.org/I4210139716"],"apc_list":{"value":2000,"currency":"CHF","value_usd":2165},"apc_paid":{"value":2000,"currency":"CHF","value_usd":2165},"fwci":2.4611,"has_fulltext":true,"cited_by_count":28,"citation_normalized_percentile":{"value":0.90131271,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"20","issue":"3","first_page":"201","last_page":"201"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10848","display_name":"Advanced Multi-Objective Optimization Algorithms","score":0.9998999834060669,"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":0.9998999834060669,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.9986000061035156,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9983999729156494,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/surrogate-model","display_name":"Surrogate model","score":0.7767810821533203},{"id":"https://openalex.org/keywords/hyperparameter","display_name":"Hyperparameter","score":0.7405712604522705},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6914747357368469},{"id":"https://openalex.org/keywords/bayesian-optimization","display_name":"Bayesian optimization","score":0.6890286207199097},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.589289665222168},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.5097777247428894},{"id":"https://openalex.org/keywords/approximate-bayesian-computation","display_name":"Approximate Bayesian computation","score":0.5095282196998596},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.49575623869895935},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.47708043456077576},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4761279225349426},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.4491904079914093},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4366081953048706},{"id":"https://openalex.org/keywords/kriging","display_name":"Kriging","score":0.4306652247905731},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.2890831232070923},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.26315850019454956},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.24352505803108215},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.12379881739616394}],"concepts":[{"id":"https://openalex.org/C131675550","wikidata":"https://www.wikidata.org/wiki/Q7646884","display_name":"Surrogate model","level":2,"score":0.7767810821533203},{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.7405712604522705},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6914747357368469},{"id":"https://openalex.org/C2778049539","wikidata":"https://www.wikidata.org/wiki/Q17002908","display_name":"Bayesian optimization","level":2,"score":0.6890286207199097},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.589289665222168},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.5097777247428894},{"id":"https://openalex.org/C2779377595","wikidata":"https://www.wikidata.org/wiki/Q21045424","display_name":"Approximate Bayesian computation","level":3,"score":0.5095282196998596},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.49575623869895935},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.47708043456077576},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4761279225349426},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.4491904079914093},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4366081953048706},{"id":"https://openalex.org/C81692654","wikidata":"https://www.wikidata.org/wiki/Q225926","display_name":"Kriging","level":2,"score":0.4306652247905731},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2890831232070923},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.26315850019454956},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.24352505803108215},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.12379881739616394},{"id":"https://openalex.org/C78458016","wikidata":"https://www.wikidata.org/wiki/Q840400","display_name":"Evolutionary biology","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","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}],"mesh":[],"locations_count":6,"locations":[{"id":"doi:10.3390/e20030201","is_oa":true,"landing_page_url":"https://doi.org/10.3390/e20030201","pdf_url":"https://www.mdpi.com/1099-4300/20/3/201/pdf?version=1521209663","source":{"id":"https://openalex.org/S195231649","display_name":"Entropy","issn_l":"1099-4300","issn":["1099-4300"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Entropy","raw_type":"journal-article"},{"id":"pmid:33265292","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/33265292","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Entropy (Basel, Switzerland)","raw_type":null},{"id":"pmh:oai:doaj.org/article:8b70602b2e2f4958866bcfbbf8d80beb","is_oa":true,"landing_page_url":"https://doaj.org/article/8b70602b2e2f4958866bcfbbf8d80beb","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Entropy, Vol 20, Iss 3, p 201 (2018)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/1099-4300/20/3/201/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/e20030201","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Entropy; Volume 20; Issue 3; Pages: 201","raw_type":"Text"},{"id":"pmh:oai:pubmedcentral.nih.gov:7512716","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/7512716","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Entropy (Basel)","raw_type":"Text"},{"id":"pmh:oai:pure.mpg.de:item_2623476","is_oa":false,"landing_page_url":"http://hdl.handle.net/21.11116/0000-0001-D556-E","pdf_url":null,"source":{"id":"https://openalex.org/S4306400654","display_name":"MPG.PuRe (Max Planck Society)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I149899117","host_organization_name":"Max Planck Society","host_organization_lineage":["https://openalex.org/I149899117"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Entropy","raw_type":"info:eu-repo/semantics/article"}],"best_oa_location":{"id":"doi:10.3390/e20030201","is_oa":true,"landing_page_url":"https://doi.org/10.3390/e20030201","pdf_url":"https://www.mdpi.com/1099-4300/20/3/201/pdf?version=1521209663","source":{"id":"https://openalex.org/S195231649","display_name":"Entropy","issn_l":"1099-4300","issn":["1099-4300"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Entropy","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W2790101447.pdf"},"referenced_works_count":32,"referenced_works":["https://openalex.org/W76947095","https://openalex.org/W1534349507","https://openalex.org/W1576375403","https://openalex.org/W1588959569","https://openalex.org/W1590183771","https://openalex.org/W1591699755","https://openalex.org/W1654787807","https://openalex.org/W1985064364","https://openalex.org/W2003381716","https://openalex.org/W2018044188","https://openalex.org/W2019130306","https://openalex.org/W2024697317","https://openalex.org/W2112485705","https://openalex.org/W2130416410","https://openalex.org/W2139929624","https://openalex.org/W2151238122","https://openalex.org/W2167986580","https://openalex.org/W2239718498","https://openalex.org/W2592279581","https://openalex.org/W2860329578","https://openalex.org/W2890351024","https://openalex.org/W4205686602","https://openalex.org/W4211049957","https://openalex.org/W4236876623","https://openalex.org/W4254772338","https://openalex.org/W6629131304","https://openalex.org/W6654769447","https://openalex.org/W6675590206","https://openalex.org/W6684921082","https://openalex.org/W6690133358","https://openalex.org/W6754573523","https://openalex.org/W6981926866"],"related_works":["https://openalex.org/W4233965824","https://openalex.org/W2607442583","https://openalex.org/W2931365531","https://openalex.org/W3199199693","https://openalex.org/W4286970057","https://openalex.org/W2995583237","https://openalex.org/W2967237190","https://openalex.org/W4323366554","https://openalex.org/W3028852288","https://openalex.org/W4221150662"],"abstract_inverted_index":{"The":[0,242],"simulation":[1],"of":[2,79,93,110,128,158,212,259,261],"complex":[3],"physics":[4],"models":[5],"may":[6,113],"lead":[7],"to":[8,21,70,76,102,165,176,192,197,208,246,256],"enormous":[9],"computer":[10,68],"running":[11],"times.":[12],"Since":[13],"the":[14,23,27,77,83,91,104,107,111,122,126,147,159,162,172,183,188,210,220,224,229,233,240,262],"simulations":[15,69],"are":[16,151,217],"expensive":[17],"it":[18,142],"is":[19,132,143,174,244],"necessary":[20],"exploit":[22],"computational":[24],"budget":[25],"in":[26,49,154,228,249,254],"best":[28],"possible":[29],"manner.":[30],"If":[31],"for":[32,56,66,90,187],"a":[33,54,94,138,155,177],"few":[34],"input":[35,63],"parameter":[36,64,185],"settings":[37],"an":[38,58,62],"output":[39],"data":[40,52,190,215,248],"set":[41,65,186],"has":[42],"been":[43],"acquired,":[44],"one":[45,250],"could":[46],"be":[47,114],"interested":[48,153],"taking":[50],"these":[51],"as":[53],"basis":[55],"finding":[57],"extremum":[59,181],"and":[60,204,251],"possibly":[61],"further":[67],"determine":[71],"it-a":[72],"task":[73],"which":[74],"belongs":[75],"realm":[78],"global":[80],"optimization.":[81],"Within":[82],"Bayesian":[84],"framework":[85],"we":[86,120,150,195],"utilize":[87],"Gaussian":[88],"processes":[89],"creation":[92],"surrogate":[95,163,173,234],"model":[96,160,221,235],"function":[97,179,222],"adjusted":[98],"self-consistently":[99],"via":[100],"hyperparameters":[101,112],"represent":[103],"data.":[105],"Although":[106],"probability":[108],"distribution":[109],"widely":[115],"spread":[116],"over":[117],"phase":[118],"space,":[119],"make":[121],"assumption":[123],"that":[124,149],"only":[125],"use":[127],"their":[129],"expectation":[130],"values":[131],"sufficient.":[133],"While":[134],"this":[135,171],"shortcut":[136],"facilitates":[137],"quickly":[139],"accessible":[140],"surrogate,":[141],"somewhat":[144],"justified":[145],"by":[146,161],"fact":[148],"not":[152,237],"full":[156],"representation":[157],"but":[164],"reveal":[166],"its":[167],"maximum.":[168],"To":[169],"accomplish":[170],"fed":[175],"utility":[178,201],"whose":[180],"determines":[182],"new":[184],"next":[189],"point":[191],"obtain.":[193],"Moreover,":[194],"propose":[196],"alternate":[198],"between":[199],"two":[200,252],"functions-expected":[202],"improvement":[203],"maximum":[205],"variance-in":[206],"order":[207,255],"avoid":[209],"drawbacks":[211],"each.":[213],"Subsequent":[214],"points":[216,230],"drawn":[218],"from":[219],"until":[223],"procedure":[225,243],"either":[226],"remains":[227],"found":[231],"or":[232],"does":[236],"change":[238],"with":[239],"iteration.":[241],"applied":[245],"mock":[247],"dimensions":[253],"demonstrate":[257],"proof":[258],"principle":[260],"proposed":[263],"approach.":[264]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":7},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":4},{"year":2018,"cited_by_count":1}],"updated_date":"2026-07-15T18:14:33.161393","created_date":"2025-10-10T00:00:00"}