{"id":"https://openalex.org/W4312575059","doi":"https://doi.org/10.1109/ijcnn55064.2022.9892767","title":"Deep Monte Carlo Quantile Regression for Quantifying Aleatoric Uncertainty in Physics-informed Temperature Field Reconstruction","display_name":"Deep Monte Carlo Quantile Regression for Quantifying Aleatoric Uncertainty in Physics-informed Temperature Field Reconstruction","publication_year":2022,"publication_date":"2022-07-18","ids":{"openalex":"https://openalex.org/W4312575059","doi":"https://doi.org/10.1109/ijcnn55064.2022.9892767"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn55064.2022.9892767","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn55064.2022.9892767","pdf_url":null,"source":{"id":"https://openalex.org/S4363607707","display_name":"2022 International Joint Conference on Neural Networks (IJCNN)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"article","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/A5103268977","display_name":"Xiaohu Zheng","orcid":"https://orcid.org/0000-0003-4568-4277"},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xiaohu Zheng","raw_affiliation_strings":["College of Aerospace Science and Engineering, National University of Defense Technology,Changsha,China","College of Aerospace Science and Engineering, National University of Defense Technology, Changsha, China"],"affiliations":[{"raw_affiliation_string":"College of Aerospace Science and Engineering, National University of Defense Technology,Changsha,China","institution_ids":["https://openalex.org/I170215575"]},{"raw_affiliation_string":"College of Aerospace Science and Engineering, National University of Defense Technology, Changsha, China","institution_ids":["https://openalex.org/I170215575"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089866078","display_name":"Wen Yao","orcid":"https://orcid.org/0000-0001-5224-9834"},"institutions":[{"id":"https://openalex.org/I4210160531","display_name":"Chinese People's Liberation Army","ror":"https://ror.org/05tf9r976","country_code":"CN","type":"funder","lineage":["https://openalex.org/I4210160531"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wen Yao","raw_affiliation_strings":["Defense Innovation Institute, Chinese Academy of Military Science,Beijing,China","Defense Innovation Institute, Chinese Academy of Military Science, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Defense Innovation Institute, Chinese Academy of Military Science,Beijing,China","institution_ids":["https://openalex.org/I4210160531"]},{"raw_affiliation_string":"Defense Innovation Institute, Chinese Academy of Military Science, Beijing, China","institution_ids":["https://openalex.org/I4210160531"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074772585","display_name":"Zhiqiang Gong","orcid":"https://orcid.org/0000-0001-7999-3014"},"institutions":[{"id":"https://openalex.org/I4210160531","display_name":"Chinese People's Liberation Army","ror":"https://ror.org/05tf9r976","country_code":"CN","type":"funder","lineage":["https://openalex.org/I4210160531"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhiqiang Gong","raw_affiliation_strings":["Defense Innovation Institute, Chinese Academy of Military Science,Beijing,China","Defense Innovation Institute, Chinese Academy of Military Science, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Defense Innovation Institute, Chinese Academy of Military Science,Beijing,China","institution_ids":["https://openalex.org/I4210160531"]},{"raw_affiliation_string":"Defense Innovation Institute, Chinese Academy of Military Science, Beijing, China","institution_ids":["https://openalex.org/I4210160531"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5115595819","display_name":"Yunyang Zhang","orcid":"https://orcid.org/0009-0001-1305-1865"},"institutions":[{"id":"https://openalex.org/I4210160531","display_name":"Chinese People's Liberation Army","ror":"https://ror.org/05tf9r976","country_code":"CN","type":"funder","lineage":["https://openalex.org/I4210160531"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yunyang Zhang","raw_affiliation_strings":["Defense Innovation Institute, Chinese Academy of Military Science,Beijing,China","Defense Innovation Institute, Chinese Academy of Military Science, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Defense Innovation Institute, Chinese Academy of Military Science,Beijing,China","institution_ids":["https://openalex.org/I4210160531"]},{"raw_affiliation_string":"Defense Innovation Institute, Chinese Academy of Military Science, Beijing, China","institution_ids":["https://openalex.org/I4210160531"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101949639","display_name":"Xiaoyu Zhao","orcid":"https://orcid.org/0000-0002-6689-5260"},"institutions":[{"id":"https://openalex.org/I4210160531","display_name":"Chinese People's Liberation Army","ror":"https://ror.org/05tf9r976","country_code":"CN","type":"funder","lineage":["https://openalex.org/I4210160531"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoyu Zhao","raw_affiliation_strings":["Defense Innovation Institute, Chinese Academy of Military Science,Beijing,China","Defense Innovation Institute, Chinese Academy of Military Science, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Defense Innovation Institute, Chinese Academy of Military Science,Beijing,China","institution_ids":["https://openalex.org/I4210160531"]},{"raw_affiliation_string":"Defense Innovation Institute, Chinese Academy of Military Science, Beijing, China","institution_ids":["https://openalex.org/I4210160531"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5068914676","display_name":"Tingsong Jiang","orcid":"https://orcid.org/0000-0003-1637-2928"},"institutions":[{"id":"https://openalex.org/I4210160531","display_name":"Chinese People's Liberation Army","ror":"https://ror.org/05tf9r976","country_code":"CN","type":"funder","lineage":["https://openalex.org/I4210160531"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tingsong Jiang","raw_affiliation_strings":["Defense Innovation Institute, Chinese Academy of Military Science,Beijing,China","Defense Innovation Institute, Chinese Academy of Military Science, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Defense Innovation Institute, Chinese Academy of Military Science,Beijing,China","institution_ids":["https://openalex.org/I4210160531"]},{"raw_affiliation_string":"Defense Innovation Institute, Chinese Academy of Military Science, Beijing, China","institution_ids":["https://openalex.org/I4210160531"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5103268977"],"corresponding_institution_ids":["https://openalex.org/I170215575"],"apc_list":null,"apc_paid":null,"fwci":0.5823,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.61603774,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11206","display_name":"Model Reduction and Neural Networks","score":0.9753999710083008,"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"}},"topics":[{"id":"https://openalex.org/T11206","display_name":"Model Reduction and Neural Networks","score":0.9753999710083008,"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/T10934","display_name":"Heat shock proteins research","score":0.9606000185012817,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T12019","display_name":"Calibration and Measurement Techniques","score":0.9232000112533569,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7750481367111206},{"id":"https://openalex.org/keywords/quantile-regression","display_name":"Quantile regression","score":0.739872932434082},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.7044300436973572},{"id":"https://openalex.org/keywords/quantile","display_name":"Quantile","score":0.6965093612670898},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6046426296234131},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5827633142471313},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.582626461982727},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.532416820526123},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.4798702299594879},{"id":"https://openalex.org/keywords/uncertainty-quantification","display_name":"Uncertainty quantification","score":0.474616676568985},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4454032778739929},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4000913202762604},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.36158400774002075},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.34016168117523193},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.2882040739059448},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.242177814245224},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1889152228832245}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7750481367111206},{"id":"https://openalex.org/C63817138","wikidata":"https://www.wikidata.org/wiki/Q3455889","display_name":"Quantile regression","level":2,"score":0.739872932434082},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.7044300436973572},{"id":"https://openalex.org/C118671147","wikidata":"https://www.wikidata.org/wiki/Q578714","display_name":"Quantile","level":2,"score":0.6965093612670898},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6046426296234131},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5827633142471313},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.582626461982727},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.532416820526123},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.4798702299594879},{"id":"https://openalex.org/C32230216","wikidata":"https://www.wikidata.org/wiki/Q7882499","display_name":"Uncertainty quantification","level":2,"score":0.474616676568985},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4454032778739929},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4000913202762604},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36158400774002075},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.34016168117523193},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2882040739059448},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.242177814245224},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1889152228832245},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn55064.2022.9892767","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn55064.2022.9892767","pdf_url":null,"source":{"id":"https://openalex.org/S4363607707","display_name":"2022 International Joint Conference on Neural Networks (IJCNN)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2404692483","display_name":null,"funder_award_id":"11725211","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1901129140","https://openalex.org/W1981463702","https://openalex.org/W2047973167","https://openalex.org/W2103559027","https://openalex.org/W2167250202","https://openalex.org/W2600383743","https://openalex.org/W2937629544","https://openalex.org/W2943171176","https://openalex.org/W2963238274","https://openalex.org/W2971442502","https://openalex.org/W2981482273","https://openalex.org/W3027546675","https://openalex.org/W3045340506","https://openalex.org/W3097124738","https://openalex.org/W3111914315","https://openalex.org/W3152851547","https://openalex.org/W3160411383","https://openalex.org/W3177253315","https://openalex.org/W3194244462","https://openalex.org/W3196881815","https://openalex.org/W4288026164","https://openalex.org/W6631190155","https://openalex.org/W6639824700","https://openalex.org/W6684388392","https://openalex.org/W6730042731","https://openalex.org/W6735443497","https://openalex.org/W6762187592","https://openalex.org/W6767328938","https://openalex.org/W6767572230"],"related_works":["https://openalex.org/W4206511378","https://openalex.org/W4206618949","https://openalex.org/W2526321210","https://openalex.org/W3205863630","https://openalex.org/W4318833145","https://openalex.org/W2364275385","https://openalex.org/W4388704167","https://openalex.org/W2007977664","https://openalex.org/W4376874882","https://openalex.org/W2224749288"],"abstract_inverted_index":{"For":[0],"the":[1,11,23,43,49,59,69,90,102,105,114,119,136,139,156,170,174,177,188],"temperature":[2,91],"field":[3,92],"reconstruction":[4],"(TFR),":[5],"a":[6,17,32,79,144],"complex":[7],"image-to-image":[8],"regression":[9,84],"problem,":[10],"convolutional":[12,24],"neural":[13],"network":[14],"(CNN)":[15],"is":[16,37,65,182,195],"powerful":[18],"surrogate":[19,128],"model":[20,129,159],"due":[21],"to":[22,39,112],"layer's":[25],"good":[26],"image":[27,147,167],"feature":[28],"extraction":[29],"ability.":[30],"However,":[31],"lot":[33],"of":[34,116,176,190],"labeled":[35,62,132],"data":[36,54,64,99,191],"needed":[38],"train":[40],"CNN,":[41],"and":[42,61,93,187],"common":[44],"CNN":[45,158],"can":[46,123,160],"not":[47],"quantify":[48,161],"aleatoric":[50,95,162],"uncertainty":[51,96,163],"caused":[52,97],"by":[53,98,164,184],"noise.":[55,100],"In":[56],"actual":[57],"engineering,":[58],"noiseless":[60],"training":[63,115,133,153],"hardly":[66],"obtained":[67],"for":[68,88,148],"TFR.":[70],"To":[71],"solve":[72],"these":[73],"two":[74],"problems,":[75],"this":[76],"paper":[77],"proposes":[78],"deep":[80],"Monte":[81],"Carlo":[82],"quantile":[83,145,165],"(Deep":[85],"MC-QR)":[86],"method":[87,108,122,142,181],"reconstructing":[89],"quantifying":[94],"On":[101,135],"one":[103],"hand,":[104,138],"Deep":[106,120,140,179],"MC-QR":[107,121,141,180],"uses":[109],"physical":[110],"knowledge":[111],"guide":[113],"CNN.":[117],"Thereby,":[118],"reconstruct":[124],"an":[125],"accurate":[126],"TFR":[127,194],"without":[130],"any":[131],"data.":[134],"other":[137],"constructs":[143],"level":[146,166],"each":[149,152],"input":[150],"in":[151],"epoch.":[154],"Then,":[155],"trained":[157],"sampling":[168],"during":[169],"prediction":[171],"stage.":[172],"Finally,":[173],"effectiveness":[175],"proposed":[178],"validated":[183],"many":[185],"experiments,":[186],"influence":[189],"noise":[192],"on":[193],"analyzed.":[196]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2026-01-13T01:12:25.745995","created_date":"2025-10-10T00:00:00"}
