{"id":"https://openalex.org/W4416102218","doi":"https://doi.org/10.48550/arxiv.2507.06533","title":"From large-eddy simulations to deep learning: A U-net model for fast urban canopy flow predictions","display_name":"From large-eddy simulations to deep learning: A U-net model for fast urban canopy flow predictions","publication_year":2025,"publication_date":"2025-07-09","ids":{"openalex":"https://openalex.org/W4416102218","doi":"https://doi.org/10.48550/arxiv.2507.06533"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2507.06533","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2507.06533","pdf_url":"https://arxiv.org/pdf/2507.06533","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"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2507.06533","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5037246651","display_name":"Themistoklis Vargiemezis","orcid":"https://orcid.org/0000-0001-5607-025X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Vargiemezis, Themistoklis","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5041714466","display_name":"Catherine Gorl\u00e9","orcid":"https://orcid.org/0000-0001-8281-6545"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gorl\u00e9, Catherine","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"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/T11371","display_name":"Wind and Air Flow Studies","score":0.9908999800682068,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11371","display_name":"Wind and Air Flow Studies","score":0.9908999800682068,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10466","display_name":"Meteorological Phenomena and Simulations","score":0.00139999995008111,"subfield":{"id":"https://openalex.org/subfields/1902","display_name":"Atmospheric Science"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11254","display_name":"Fluid Dynamics and Vibration Analysis","score":0.0008999999845400453,"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/computation","display_name":"Computation","score":0.5823000073432922},{"id":"https://openalex.org/keywords/wind-speed","display_name":"Wind speed","score":0.5763000249862671},{"id":"https://openalex.org/keywords/turbulence","display_name":"Turbulence","score":0.5659999847412109},{"id":"https://openalex.org/keywords/flow","display_name":"Flow (mathematics)","score":0.4666000008583069},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.46149998903274536},{"id":"https://openalex.org/keywords/computational-fluid-dynamics","display_name":"Computational fluid dynamics","score":0.45339998602867126},{"id":"https://openalex.org/keywords/approximation-error","display_name":"Approximation error","score":0.4325999915599823},{"id":"https://openalex.org/keywords/wind-direction","display_name":"Wind direction","score":0.4059000015258789},{"id":"https://openalex.org/keywords/magnitude","display_name":"Magnitude (astronomy)","score":0.3840999901294708}],"concepts":[{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.5823000073432922},{"id":"https://openalex.org/C161067210","wikidata":"https://www.wikidata.org/wiki/Q1464943","display_name":"Wind speed","level":2,"score":0.5763000249862671},{"id":"https://openalex.org/C196558001","wikidata":"https://www.wikidata.org/wiki/Q190132","display_name":"Turbulence","level":2,"score":0.5659999847412109},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.4781999886035919},{"id":"https://openalex.org/C38349280","wikidata":"https://www.wikidata.org/wiki/Q1434290","display_name":"Flow (mathematics)","level":2,"score":0.4666000008583069},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.46149998903274536},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4578999876976013},{"id":"https://openalex.org/C1633027","wikidata":"https://www.wikidata.org/wiki/Q815820","display_name":"Computational fluid dynamics","level":2,"score":0.45339998602867126},{"id":"https://openalex.org/C122383733","wikidata":"https://www.wikidata.org/wiki/Q865920","display_name":"Approximation error","level":2,"score":0.4325999915599823},{"id":"https://openalex.org/C39432304","wikidata":"https://www.wikidata.org/wiki/Q188847","display_name":"Environmental science","level":0,"score":0.4268999993801117},{"id":"https://openalex.org/C107775477","wikidata":"https://www.wikidata.org/wiki/Q1057900","display_name":"Wind direction","level":3,"score":0.4059000015258789},{"id":"https://openalex.org/C126691448","wikidata":"https://www.wikidata.org/wiki/Q2028919","display_name":"Magnitude (astronomy)","level":2,"score":0.3840999901294708},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.36250001192092896},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3610000014305115},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3476000130176544},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.3456999957561493},{"id":"https://openalex.org/C118365302","wikidata":"https://www.wikidata.org/wiki/Q4817115","display_name":"Atmospheric model","level":2,"score":0.31779998540878296},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.31610000133514404},{"id":"https://openalex.org/C44154836","wikidata":"https://www.wikidata.org/wiki/Q45045","display_name":"Simulation","level":1,"score":0.314300000667572},{"id":"https://openalex.org/C205904022","wikidata":"https://www.wikidata.org/wiki/Q6803620","display_name":"Mean flow","level":3,"score":0.30320000648498535},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.3027999997138977},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.29829999804496765},{"id":"https://openalex.org/C81299745","wikidata":"https://www.wikidata.org/wiki/Q334269","display_name":"Transfer function","level":2,"score":0.29109999537467957},{"id":"https://openalex.org/C166693061","wikidata":"https://www.wikidata.org/wiki/Q5462119","display_name":"Flow velocity","level":3,"score":0.2881999909877777},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.2818000018596649},{"id":"https://openalex.org/C2778368647","wikidata":"https://www.wikidata.org/wiki/Q702492","display_name":"Urban area","level":2,"score":0.27720001339912415},{"id":"https://openalex.org/C15476950","wikidata":"https://www.wikidata.org/wiki/Q7854776","display_name":"Turbulence kinetic energy","level":3,"score":0.2768999934196472},{"id":"https://openalex.org/C199104240","wikidata":"https://www.wikidata.org/wiki/Q118291","display_name":"Marine engineering","level":1,"score":0.2678999900817871},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.2587999999523163}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2507.06533","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2507.06533","pdf_url":"https://arxiv.org/pdf/2507.06533","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"},{"id":"doi:10.48550/arxiv.2507.06533","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2507.06533","pdf_url":null,"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":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2507.06533","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2507.06533","pdf_url":"https://arxiv.org/pdf/2507.06533","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":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Accurate":[0],"prediction":[1],"of":[2,58,69,82,123,180,203,218],"wind":[3,23,60,109,227],"flow":[4,61],"fields":[5],"in":[6,114],"urban":[7,18,59,105,137,226,235],"canopies":[8],"is":[9,165,238],"crucial":[10],"for":[11,53,75,167,205,210,230],"ensuring":[12],"pedestrian":[13],"comfort,":[14],"safety,":[15],"and":[16,25,41,55,128,150,185,208,233],"sustainable":[17],"design.":[19],"Traditional":[20],"methods":[21],"using":[22,89],"tunnels":[24],"Computational":[26],"Fluid":[27],"Dynamics,":[28],"such":[29],"as":[30,153],"Large-Eddy":[31],"Simulations":[32],"(LES),":[33],"are":[34],"limited":[35],"by":[36],"high":[37,195],"costs,":[38],"computational":[39],"demands,":[40],"time":[42,65],"requirements.":[43],"This":[44,213],"study":[45],"presents":[46],"a":[47,86,95,156,161],"deep":[48,219],"neural":[49],"network":[50],"(DNN)":[51],"approach":[52],"fast":[54],"accurate":[56,225],"predictions":[57],"fields,":[62],"reducing":[63],"computation":[64],"from":[66],"an":[67,80,198],"order":[68,81],"10":[70],"hours":[71],"on":[72,85,99,190],"32":[73],"CPUs":[74],"one":[76],"LES":[77,100],"evaluation":[78,189],"to":[79,112,222],"1":[83],"second":[84],"single":[87],"GPU":[88],"the":[90,136,177,216],"DNN":[91],"model.":[92],"We":[93],"employ":[94],"U-Net":[96],"architecture":[97],"trained":[98],"data":[101],"including":[102],"252":[103],"synthetic":[104],"configurations":[106],"at":[107,132,240],"seven":[108],"directions":[110],"($0^{o}$":[111],"$90^{o}$":[113],"$15^{o}$":[115],"increments).":[116],"The":[117,139,173],"model":[118],"predicts":[119],"two":[120],"key":[121],"quantities":[122],"interest:":[124],"mean":[125,200],"velocity":[126,206],"magnitude":[127,207],"streamwise":[129],"turbulence":[130,211],"intensity,":[131],"multiple":[133],"heights":[134],"within":[135],"canopy.":[138],"U-net":[140],"uses":[141],"2D":[142],"building":[143],"representations":[144],"augmented":[145],"with":[146,197],"signed":[147],"distance":[148],"functions":[149],"their":[151,182],"gradients":[152],"inputs,":[154],"forming":[155],"$256\\times256\\times9$":[157],"tensor.":[158],"In":[159],"addition,":[160],"Spatial":[162],"Attention":[163],"Module":[164],"used":[166],"feature":[168],"transfer":[169],"through":[170],"skip":[171],"connections.":[172],"loss":[174],"function":[175],"combines":[176],"root-mean-square":[178],"error":[179,202],"predictions,":[181],"gradient":[183],"magnitudes,":[184],"L2":[186],"regularization.":[187],"Model":[188],"50":[191],"test":[192],"cases":[193],"demonstrates":[194],"accuracy":[196],"overall":[199],"relative":[201],"9.3%":[204],"5.2%":[209],"intensity.":[212],"research":[214],"shows":[215],"potential":[217],"learning":[220],"approaches":[221],"provide":[223],"fast,":[224],"assessments":[228],"essential":[229],"creating":[231],"comfortable":[232],"safe":[234],"environments.":[236],"Code":[237],"available":[239],"https://github.com/tvarg/Urban-FlowUnet.git":[241]},"counts_by_year":[],"updated_date":"2026-07-03T08:13:44.112507","created_date":"2025-10-10T00:00:00"}
