{"id":"https://openalex.org/W4408199516","doi":"https://doi.org/10.1109/cisp-bmei64163.2024.10906178","title":"MANet: A Deep Learning Framework for Flow Field Prediction","display_name":"MANet: A Deep Learning Framework for Flow Field Prediction","publication_year":2024,"publication_date":"2024-10-26","ids":{"openalex":"https://openalex.org/W4408199516","doi":"https://doi.org/10.1109/cisp-bmei64163.2024.10906178"},"language":"en","primary_location":{"id":"doi:10.1109/cisp-bmei64163.2024.10906178","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cisp-bmei64163.2024.10906178","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 17th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","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/A5020264645","display_name":"Luwei Xiao","orcid":"https://orcid.org/0000-0001-7229-2741"},"institutions":[{"id":"https://openalex.org/I1297991670","display_name":"Southwest University of Science and Technology","ror":"https://ror.org/04d996474","country_code":"CN","type":"education","lineage":["https://openalex.org/I1297991670"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"LiWei Xiao","raw_affiliation_strings":["School of Computer Science and Technology, Southwest University of Science and Technology"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Southwest University of Science and Technology","institution_ids":["https://openalex.org/I1297991670"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101833558","display_name":"Yonghui Chen","orcid":"https://orcid.org/0000-0001-8641-0702"},"institutions":[{"id":"https://openalex.org/I1297991670","display_name":"Southwest University of Science and Technology","ror":"https://ror.org/04d996474","country_code":"CN","type":"education","lineage":["https://openalex.org/I1297991670"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"YongHui Chen","raw_affiliation_strings":["School of Computer Science and Technology, Southwest University of Science and Technology"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Southwest University of Science and Technology","institution_ids":["https://openalex.org/I1297991670"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033955515","display_name":"Fupan Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I1297991670","display_name":"Southwest University of Science and Technology","ror":"https://ror.org/04d996474","country_code":"CN","type":"education","lineage":["https://openalex.org/I1297991670"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"FuPan Wang","raw_affiliation_strings":["School of Computer Science and Technology, Southwest University of Science and Technology"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Southwest University of Science and Technology","institution_ids":["https://openalex.org/I1297991670"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5047425939","display_name":"Shuai Fan","orcid":null},"institutions":[{"id":"https://openalex.org/I1297991670","display_name":"Southwest University of Science and Technology","ror":"https://ror.org/04d996474","country_code":"CN","type":"education","lineage":["https://openalex.org/I1297991670"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuai Fan","raw_affiliation_strings":["School of Computer Science and Technology, Southwest University of Science and Technology"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Southwest University of Science and Technology","institution_ids":["https://openalex.org/I1297991670"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5020264645"],"corresponding_institution_ids":["https://openalex.org/I1297991670"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.2987405,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9362999796867371,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9362999796867371,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11751","display_name":"Lattice Boltzmann Simulation Studies","score":0.9132999777793884,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"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/computer-science","display_name":"Computer science","score":0.6615302562713623},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.5411415100097656},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5179730653762817},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4358757436275482},{"id":"https://openalex.org/keywords/flow","display_name":"Flow (mathematics)","score":0.43220919370651245},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.327905535697937},{"id":"https://openalex.org/keywords/mechanics","display_name":"Mechanics","score":0.08058682084083557},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.07507514953613281}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6615302562713623},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.5411415100097656},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5179730653762817},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4358757436275482},{"id":"https://openalex.org/C38349280","wikidata":"https://www.wikidata.org/wiki/Q1434290","display_name":"Flow (mathematics)","level":2,"score":0.43220919370651245},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.327905535697937},{"id":"https://openalex.org/C57879066","wikidata":"https://www.wikidata.org/wiki/Q41217","display_name":"Mechanics","level":1,"score":0.08058682084083557},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.07507514953613281},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cisp-bmei64163.2024.10906178","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cisp-bmei64163.2024.10906178","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 17th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W2515505748","https://openalex.org/W2902480423","https://openalex.org/W2946771678","https://openalex.org/W2950883932","https://openalex.org/W2964277698","https://openalex.org/W2985630280","https://openalex.org/W3038687103","https://openalex.org/W3097182846","https://openalex.org/W3112161375","https://openalex.org/W3193308275","https://openalex.org/W3203014670","https://openalex.org/W3204011834","https://openalex.org/W4281661769","https://openalex.org/W4283736018","https://openalex.org/W4304083985","https://openalex.org/W4384932674"],"related_works":["https://openalex.org/W2731899572","https://openalex.org/W2961085424","https://openalex.org/W3215138031","https://openalex.org/W4306674287","https://openalex.org/W3009238340","https://openalex.org/W4360585206","https://openalex.org/W4321369474","https://openalex.org/W4285208911","https://openalex.org/W4387369504","https://openalex.org/W3046775127"],"abstract_inverted_index":{"Traditional":[0],"methods":[1],"for":[2,47,64],"predicting":[3],"airfoil":[4],"flow":[5,66],"fields":[6],"primarily":[7],"rely":[8],"on":[9],"computational":[10,31],"fluid":[11],"dynamics":[12],"(CFD)":[13],"simulations":[14],"and":[15,30,40,87,104,118,146],"wind":[16,34],"tunnel":[17,35],"experiments.":[18],"However,":[19],"solving":[20],"the":[21,120,155],"N":[22],"avier-Stokes":[23],"(NS)":[24],"equations":[25],"typically":[26],"requires":[27],"significant":[28],"memory":[29],"cost,":[32],"while":[33,73],"experiments":[36,128],"demand":[37],"substantial":[38],"human":[39],"financial":[41],"resources.":[42],"This":[43,107],"presents":[44],"a":[45,60,91],"bottleneck":[46],"large-scale":[48],"engineering":[49],"designs,":[50],"such":[51],"as":[52],"aerodynamic":[53],"shape":[54],"optimization.":[55],"Deep":[56],"learning":[57,80],"models":[58],"offer":[59],"highly":[61],"efficient":[62],"solution":[63],"rapid":[65],"field":[67],"simulation":[68],"by":[69,112,136,144,151],"significantly":[70],"reducing":[71],"costs":[72],"maintaining":[74],"high":[75],"accuracy.":[76],"A":[77],"novel":[78],"deep":[79],"model,":[81],"which":[82],"integrates":[83],"convolutional":[84],"attention":[85],"mechanisms":[86],"multiple":[88,124],"decoders":[89],"into":[90],"modified":[92],"U-N":[93],"et":[94],"network,":[95],"has":[96],"been":[97],"proposed":[98],"to":[99,154],"further":[100],"enhance":[101],"model":[102,108],"precision":[103],"lightweight":[105],"performance.":[106],"reduces":[109,133],"parameter":[110,134],"size":[111,135],"75%,":[113],"improves":[114],"feature":[115],"extraction":[116],"capability,":[117],"mitigates":[119],"mutual":[121],"influence":[122],"between":[123],"prediction":[125],"variables.":[126],"Comparative":[127],"show":[129],"that":[130],"this":[131],"approach":[132],"about":[137],"42":[138],"%,":[139],"mean":[140,147],"absolute":[141],"error":[142,149],"(MAE)":[143],"20%,":[145],"squared":[148],"(MSE)":[150],"12%":[152],"compared":[153],"traditional":[156],"U-Net":[157],"network.":[158]},"counts_by_year":[],"updated_date":"2025-12-28T23:10:05.387466","created_date":"2025-10-10T00:00:00"}
