{"id":"https://openalex.org/W7224900508","doi":"https://doi.org/10.48550/arxiv.2610.39789","title":"Data-Driven or Model-Constrained? Comparing Airflow Estimation Methods under Sparse Sensing","display_name":"Data-Driven or Model-Constrained? Comparing Airflow Estimation Methods under Sparse Sensing","publication_year":2026,"publication_date":"2026-10-29","ids":{"openalex":"https://openalex.org/W7224900508","doi":"https://doi.org/10.48550/arxiv.2610.39789"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2610.39789","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2610.39789","pdf_url":"https://arxiv.org/pdf/2610.39789","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2610.39789","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5113316582","display_name":"Dominik Aigner","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I62916508","display_name":"Technical University of Munich","ror":"https://ror.org/02kkvpp62","country_code":"DE","type":"education","lineage":["https://openalex.org/I62916508"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Aigner, Dominik","raw_affiliation_strings":["Technical University of Munich ,"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Technical University of Munich ,","institution_ids":["https://openalex.org/I62916508"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018138101","display_name":"Thomas Wiedemann","orcid":"https://orcid.org/0000-0002-1740-8841","observed_orcids":["https://orcid.org/0000-0002-1740-8841"]},"institutions":[{"id":"https://openalex.org/I2898391981","display_name":"Deutsches Zentrum f\u00fcr Luft- und Raumfahrt e. V. (DLR)","ror":"https://ror.org/04bwf3e34","country_code":"DE","type":"facility","lineage":["https://openalex.org/I1305996414","https://openalex.org/I2898391981"]},{"id":"https://openalex.org/I62916508","display_name":"Technical University of Munich","ror":"https://ror.org/02kkvpp62","country_code":"DE","type":"education","lineage":["https://openalex.org/I62916508"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Wiedemann, Thomas","raw_affiliation_strings":["German Aerospace Center ,","Technical University of Munich ,"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"German Aerospace Center ,","institution_ids":["https://openalex.org/I2898391981"]},{"raw_affiliation_string":"Technical University of Munich ,","institution_ids":["https://openalex.org/I62916508"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020082464","display_name":"Javier Monroy","orcid":"https://orcid.org/0000-0001-7869-7811","observed_orcids":["https://orcid.org/0000-0001-7869-7811"]},"institutions":[{"id":"https://openalex.org/I82767444","display_name":"Universidad de M\u00e1laga","ror":"https://ror.org/036b2ww28","country_code":"ES","type":"education","lineage":["https://openalex.org/I82767444"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Monroy, Javier","raw_affiliation_strings":["University of M\u00e1laga"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of M\u00e1laga","institution_ids":["https://openalex.org/I82767444"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5088586617","display_name":"Achim J. Lilienthal","orcid":"https://orcid.org/0000-0003-0217-9326","observed_orcids":["https://orcid.org/0000-0003-0217-9326"]},"institutions":[{"id":"https://openalex.org/I62916508","display_name":"Technical University of Munich","ror":"https://ror.org/02kkvpp62","country_code":"DE","type":"education","lineage":["https://openalex.org/I62916508"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Lilienthal, Achim J.","raw_affiliation_strings":["Technical University of Munich ,"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Technical University of Munich ,","institution_ids":["https://openalex.org/I62916508"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"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/T11206","display_name":"Model Reduction and Neural Networks","score":0.49540001153945923,"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":0.49540001153945923,"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/T11371","display_name":"Wind and Air Flow Studies","score":0.3253999948501587,"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/T12120","display_name":"Air Quality Monitoring and Forecasting","score":0.06400000303983688,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/sparse-sensing","display_name":"sparse sensing","score":0.8009999990463257},{"id":"https://openalex.org/keywords/physics-informed-priors","display_name":"physics-informed priors","score":0.7429999709129333},{"id":"https://openalex.org/keywords/gaussian-process-regression","display_name":"Gaussian process regression","score":0.8690000176429749},{"id":"https://openalex.org/keywords/gaussian-markov-random-fields","display_name":"Gaussian Markov random fields","score":0.9129999876022339},{"id":"https://openalex.org/keywords/ventilation-monitoring","display_name":"ventilation monitoring","score":0.7369999885559082},{"id":"https://openalex.org/keywords/gas-source-localization","display_name":"gas source localization","score":0.7279999852180481},{"id":"https://openalex.org/keywords/pollutant-dispersion","display_name":"pollutant dispersion","score":0.875},{"id":"https://openalex.org/keywords/flow-reconstruction","display_name":"flow reconstruction","score":0.5429999828338623}],"concepts":[{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.7389000058174133},{"id":"https://openalex.org/C116067010","wikidata":"https://www.wikidata.org/wiki/Q4698686","display_name":"Airflow","level":2,"score":0.718999981880188},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.5857999920845032},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.48969998955726624},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.4844000041484833},{"id":"https://openalex.org/C81692654","wikidata":"https://www.wikidata.org/wiki/Q225926","display_name":"Kriging","level":2,"score":0.47450000047683716},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.4661000072956085},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.42579999566078186},{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.39800000190734863},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.3443000018596649},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.34360000491142273},{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.3377000093460083},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3264000117778778},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.3149999976158142},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.31360000371932983},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3073999881744385},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.3003999888896942},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.29670000076293945},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.2924000024795532},{"id":"https://openalex.org/C150060386","wikidata":"https://www.wikidata.org/wiki/Q7574054","display_name":"Spatial correlation","level":2,"score":0.2903999984264374},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.28519999980926514},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.28380000591278076},{"id":"https://openalex.org/C38349280","wikidata":"https://www.wikidata.org/wiki/Q1434290","display_name":"Flow (mathematics)","level":2,"score":0.27950000762939453},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.27950000762939453},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.27219998836517334},{"id":"https://openalex.org/C39425265","wikidata":"https://www.wikidata.org/wiki/Q7098944","display_name":"Optimal estimation","level":2,"score":0.266400009393692},{"id":"https://openalex.org/C2778045648","wikidata":"https://www.wikidata.org/wiki/Q176827","display_name":"Markov random field","level":4,"score":0.2653000056743622},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.26330000162124634},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.26249998807907104},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2605000138282776},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.2587999999523163},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.2524999976158142},{"id":"https://openalex.org/C39432304","wikidata":"https://www.wikidata.org/wiki/Q188847","display_name":"Environmental science","level":0,"score":0.251800000667572},{"id":"https://openalex.org/C166550679","wikidata":"https://www.wikidata.org/wiki/Q263400","display_name":"Gaussian network model","level":3,"score":0.25060001015663147}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2610.39789","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2610.39789","pdf_url":"https://arxiv.org/pdf/2610.39789","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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.2610.39789","doi":"https://doi.org/10.48550/arxiv.2610.39789","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2610.39789","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2610.39789","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2610.39789","pdf_url":"https://arxiv.org/pdf/2610.39789","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7224900508.pdf","grobid_xml":"https://content.openalex.org/works/W7224900508.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W7167337282","https://openalex.org/W7161782543","https://openalex.org/W4283219927","https://openalex.org/W7151963373","https://openalex.org/W6943884550","https://openalex.org/W7170139957","https://openalex.org/W7084055467","https://openalex.org/W6885097940","https://openalex.org/W4368232886","https://openalex.org/W4212786708"],"abstract_inverted_index":{"Airflow-driven":[0],"advection":[1],"is":[2,37],"the":[3,78,88,155,187],"primary":[4],"mechanism":[5],"for":[6,19,59,94,164],"gas":[7,22],"and":[8,25,72,120,131],"aerosol":[9],"dispersion":[10],"in":[11,40,66],"real-world":[12],"scenarios,":[13],"making":[14],"accurate":[15],"airflow":[16,31,95,110],"estimation":[17,44,96],"essential":[18],"ventilation":[20],"monitoring,":[21],"source":[23],"localization,":[24],"contaminant":[26],"spread":[27],"analysis.":[28],"Since":[29],"obtaining":[30],"maps":[32],"via":[33],"high-resolution":[34],"spatial":[35,166],"sensing":[36,99],"usually":[38],"infeasible":[39],"practice,":[41],"flow":[42],"field":[43],"methods":[45,125],"must":[46],"cope":[47],"with":[48],"sparse":[49,98],"measurements":[50],"by":[51,162],"introducing":[52],"statistical":[53],"or":[54],"physical":[55,152],"priors":[56,93,153],"to":[57,77,137],"compensate":[58],"limited":[60],"data.":[61],"Existing":[62],"approaches":[63],"mainly":[64],"differ":[65],"how":[67],"they":[68],"incorporate":[69],"fluid-flow":[70],"constraints":[71],"environmental":[73],"geometry.":[74],"However,":[75],"due":[76],"lack":[79],"of":[80,86,91],"systematic":[81],"comparisons":[82],"across":[83,112,186],"this":[84,104],"spectrum":[85],"methods,":[87],"potential":[89],"benefits":[90],"physics-based":[92],"under":[97,117],"remain":[100],"underexplored.":[101],"To":[102],"address":[103],"question,":[105],"we":[106],"quantitatively":[107],"evaluate":[108],"four":[109],"estimators":[111,181],"three":[113],"simulated":[114],"2D":[115],"scenarios":[116],"steady-state":[118],"conditions":[119],"varying":[121],"sampling":[122,160],"densities.":[123],"The":[124],"range":[126],"from":[127],"Gaussian":[128,133,170],"Process":[129,171],"Regression":[130,172],"graph-based":[132],"Markov":[134],"Random":[135],"Fields":[136],"two":[138],"Navier-Stokes-based":[139],"approaches,":[140],"including":[141],"a":[142],"novel":[143],"strong-form":[144],"residual":[145],"minimization":[146],"variant.":[147],"Our":[148],"results":[149],"show":[150],"that":[151],"provide":[154],"greatest":[156],"benefit":[157],"at":[158],"low":[159],"densities":[161],"compensating":[163],"missing":[165],"information.":[167],"Although":[168],"data-driven":[169],"becomes":[173],"increasingly":[174],"competitive":[175],"as":[176],"sensor":[177],"coverage":[178],"increases,":[179],"model-based":[180],"retain":[182],"an":[183],"accuracy":[184],"advantage":[185],"evaluated":[188],"scenarios.":[189]},"counts_by_year":[],"updated_date":"2026-11-06T12:05:46.655746","created_date":"2026-10-31T00:00:00"}
