{"id":"https://openalex.org/W2504663153","doi":null,"title":"A neural network construction method for surrogate modeling of physics-based analysis","display_name":"A neural network construction method for surrogate modeling of physics-based analysis","publication_year":2012,"publication_date":"2012-04-04","ids":{"openalex":"https://openalex.org/W2504663153","mag":"2504663153"},"language":"en","primary_location":{"id":"pmh:oai:smartech.gatech.edu:1853/43721","doi":null,"is_oa":false,"landing_page_url":"http://hdl.handle.net/1853/43721","pdf_url":null,"source":{"id":"https://openalex.org/S4377196313","display_name":"SMARTech Repository (Georgia Institute of Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I130701444","host_organization_name":"Georgia Institute of Technology","host_organization_lineage":["https://openalex.org/I130701444"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Dissertation"},"volume":null,"type":"dissertation","is_traditional":true,"indexed_in":[],"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/A5108719440","display_name":"Woong Je Sung","orcid":"https://orcid.org/0000-0003-1915-8556","observed_orcids":["https://orcid.org/0000-0003-1915-8556"]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Woong Je Sung","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5108719440"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"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":0.0010999999940395355,"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/T12676","display_name":"Machine Learning and ELM","score":0.00019999999494757503,"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/network-pruning","display_name":"network pruning","score":0.4659999907016754},{"id":"https://openalex.org/keywords/surrogate-modeling","display_name":"surrogate models","score":0.9769999980926514},{"id":"https://openalex.org/keywords/multilayer-perceptron","display_name":"multilayer perceptron","score":0.7960000038146973},{"id":"https://openalex.org/keywords/training-efficiency","display_name":"training efficiency","score":0.4300000071525574}],"concepts":[{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6095273494720459},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4097346067428589},{"id":"https://openalex.org/C121864883","wikidata":"https://www.wikidata.org/wiki/Q677916","display_name":"Statistical physics","level":1,"score":0.35768935084342957},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.353263258934021},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.30046188831329346}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:smartech.gatech.edu:1853/43721","doi":null,"is_oa":false,"landing_page_url":"http://hdl.handle.net/1853/43721","pdf_url":null,"source":{"id":"https://openalex.org/S4377196313","display_name":"SMARTech Repository (Georgia Institute of Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I130701444","host_organization_name":"Georgia Institute of Technology","host_organization_lineage":["https://openalex.org/I130701444"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Dissertation"},{"id":"mag:2504663153","doi":null,"is_oa":false,"landing_page_url":"https://ui.adsabs.harvard.edu/abs/2012PhDT.......350S/abstract","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W7028024785","https://openalex.org/W2308522302","https://openalex.org/W2363619343","https://openalex.org/W1580736954","https://openalex.org/W1500684550","https://openalex.org/W2412104792","https://openalex.org/W2942365090","https://openalex.org/W3216195053","https://openalex.org/W7164192342","https://openalex.org/W7051515725"],"abstract_inverted_index":{"A":[0,115],"connectivity":[1,36],"adjusting":[2],"learning":[3,172],"algorithm,":[4],"Optimal":[5],"Brain":[6],"Growth":[7],"(OBG)":[8],"was":[9,50,144],"proposed.":[10],"Contrast":[11],"to":[12,52,179],"the":[13,18,26,29,54,59,71,111,147,160,180],"conventional":[14,181],"training":[15,43,98,139,183],"methods":[16],"for":[17,146,155],"Artificial":[19],"Neural":[20],"Network":[21],"(ANN)":[22],"which":[23],"focus":[24],"on":[25,69,163],"weight-only":[27,182],"optimization,":[28],"OBG":[30,72,142,167],"method":[31,143,168],"trains":[32],"both":[33],"weights":[34],"and":[35,87,125,151,154],"of":[37,58,82,90,100,129,137,159],"a":[38,41,75,79,88,91,101,126,133,138,156],"network":[39,103],"in":[40,110,174],"single":[42],"process.":[44,114],"The":[45,97,141,166],"standard":[46],"Back-Propagation":[47],"(BP)":[48],"algorithm":[49,73],"extended":[51],"exploit":[53],"error":[55],"gradient":[56],"information":[57],"latent":[60],"connection":[61,85,93],"whose":[62],"current":[63],"weight":[64,86],"has":[65],"zero":[66,95],"value.":[67],"Based":[68],"this,":[70],"makes":[74],"rational":[76],"decision":[77],"between":[78],"further":[80,112],"adjustment":[81],"an":[83,170],"existing":[84],"creation":[89],"new":[92],"having":[94],"weight.":[96],"efficiency":[99],"growing":[102],"is":[104,119],"maintained":[105],"by":[106],"freezing":[107],"stabilized":[108,116],"connections":[109],"optimization":[113],"computational":[117],"unit":[118],"also":[120],"decomposed":[121],"into":[122],"two":[123],"units":[124],"particular":[127],"set":[128],"decomposition":[130],"rules":[131],"guarantees":[132],"seamless":[134],"local":[135],"re-initialization":[136],"trajectory.":[140],"tested":[145],"multiple":[148],"canonical,":[149],"regression":[150],"classification":[152],"problems":[153],"surrogate":[157],"modeling":[158],"pressure":[161],"distribution":[162],"transonic":[164],"airfoils.":[165],"showed":[169],"improved":[171],"capability":[173],"computationally":[175],"efficient":[176],"manner":[177],"compared":[178],"using":[184],"connectivity-fixed":[185],"Multilayer":[186],"Perceptrons":[187],"(MLPs).":[188]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2020,"cited_by_count":2},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":1},{"year":2012,"cited_by_count":1}],"updated_date":"2026-11-03T10:50:19.383937","created_date":"2016-08-23T00:00:00"}