{"id":"https://openalex.org/W7134836761","doi":"https://doi.org/10.48550/arxiv.2603.06762","title":"Prediction of Steady-State Flow through Porous Media Using Machine Learning Models","display_name":"Prediction of Steady-State Flow through Porous Media Using Machine Learning Models","publication_year":2026,"publication_date":"2026-03-06","ids":{"openalex":"https://openalex.org/W7134836761","doi":"https://doi.org/10.48550/arxiv.2603.06762"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2603.06762","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","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":"repository"},"license":"publisher-specific-oa","license_id":"https://openalex.org/licenses/publisher-specific-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":null,"any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5123876781","display_name":"Jinhong Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Jinhong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5092918503","display_name":"Matei C. Ignuta-Ciuncanu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ignuta-Ciuncanu, Matei C.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128651991","display_name":"Ricardo F. Martinez-Botas","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Martinez-Botas, Ricardo F.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5123854287","display_name":"Teng Cao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cao, Teng","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/T11115","display_name":"Topology Optimization in Engineering","score":0.5196999907493591,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural Engineering"},"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/T11115","display_name":"Topology Optimization in Engineering","score":0.5196999907493591,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural Engineering"},"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/T11206","display_name":"Model Reduction and Neural Networks","score":0.17100000381469727,"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/T10848","display_name":"Advanced Multi-Objective Optimization Algorithms","score":0.050599999725818634,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/autoencoder","display_name":"Autoencoder","score":0.8443999886512756},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.6938999891281128},{"id":"https://openalex.org/keywords/porous-medium","display_name":"Porous medium","score":0.6660000085830688},{"id":"https://openalex.org/keywords/flow","display_name":"Flow (mathematics)","score":0.5055000185966492},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4487000107765198},{"id":"https://openalex.org/keywords/computational-fluid-dynamics","display_name":"Computational fluid dynamics","score":0.4115000069141388},{"id":"https://openalex.org/keywords/fluid-dynamics","display_name":"Fluid dynamics","score":0.38100001215934753},{"id":"https://openalex.org/keywords/topology","display_name":"Topology (electrical circuits)","score":0.37929999828338623}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.8443999886512756},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.6938999891281128},{"id":"https://openalex.org/C105569014","wikidata":"https://www.wikidata.org/wiki/Q3271208","display_name":"Porous medium","level":3,"score":0.6660000085830688},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6237000226974487},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5953999757766724},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5763999819755554},{"id":"https://openalex.org/C38349280","wikidata":"https://www.wikidata.org/wiki/Q1434290","display_name":"Flow (mathematics)","level":2,"score":0.5055000185966492},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4593999981880188},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4487000107765198},{"id":"https://openalex.org/C1633027","wikidata":"https://www.wikidata.org/wiki/Q815820","display_name":"Computational fluid dynamics","level":2,"score":0.4115000069141388},{"id":"https://openalex.org/C90278072","wikidata":"https://www.wikidata.org/wiki/Q216320","display_name":"Fluid dynamics","level":2,"score":0.38100001215934753},{"id":"https://openalex.org/C184720557","wikidata":"https://www.wikidata.org/wiki/Q7825049","display_name":"Topology (electrical circuits)","level":2,"score":0.37929999828338623},{"id":"https://openalex.org/C189950617","wikidata":"https://www.wikidata.org/wiki/Q937228","display_name":"Property (philosophy)","level":2,"score":0.36059999465942383},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3582000136375427},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3515999913215637},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.33379998803138733},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.3237000107765198},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.3190999925136566},{"id":"https://openalex.org/C17020691","wikidata":"https://www.wikidata.org/wiki/Q139677","display_name":"Operator (biology)","level":5,"score":0.3098999857902527},{"id":"https://openalex.org/C199845137","wikidata":"https://www.wikidata.org/wiki/Q145490","display_name":"Network topology","level":2,"score":0.2935999929904938},{"id":"https://openalex.org/C459310","wikidata":"https://www.wikidata.org/wiki/Q117801","display_name":"Computational science","level":1,"score":0.27250000834465027},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2565999925136566}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2603.06762","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","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":"repository"},"license":"publisher-specific-oa","license_id":"https://openalex.org/licenses/publisher-specific-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},{"id":"doi:10.48550/arxiv.2603.06762","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.06762","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:doi:10.48550/arxiv.2603.06762","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","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":"repository"},"license":"publisher-specific-oa","license_id":"https://openalex.org/licenses/publisher-specific-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Solving":[0],"flow":[1,66,162],"through":[2,67],"porous":[3,68,165],"media":[4,69],"is":[5],"a":[6,16,46,60,112,168],"crucial":[7],"step":[8],"in":[9,19,164],"the":[10,72,133,154],"topology":[11,142],"optimisation":[12,143],"of":[13,124,136,156],"cold":[14],"plates,":[15],"key":[17],"component":[18],"modern":[20],"thermal":[21],"management.":[22],"Traditional":[23],"computational":[24],"fluid":[25,161],"dynamics":[26],"(CFD)":[27],"methods,":[28],"while":[29,121],"accurate,":[30],"are":[31,149],"often":[32],"prohibitively":[33],"expensive":[34],"for":[35,63,141],"large":[36],"and":[37,52,77,85,109],"complex":[38],"geometries.":[39],"In":[40,55],"contrast,":[41],"data-driven":[42],"surrogate":[43],"models":[44],"provide":[45],"computationally":[47],"efficient":[48],"alternative,":[49],"enabling":[50],"rapid":[51],"reliable":[53],"predictions.":[54],"this":[56],"study,":[57],"we":[58,97],"develop":[59],"machine-learning":[61],"framework":[62],"predicting":[64],"steady-state":[65],"governed":[70],"by":[71],"Navier-Stokes-Brinkman":[73],"equations.":[74],"We":[75],"implement":[76],"compare":[78],"three":[79],"model":[80],"architectures-convolutional":[81],"autoencoder":[82],"(AE),":[83],"U-Net,":[84,110],"Fourier":[86],"Neural":[87],"Operator":[88],"(FNO)-evaluating":[89],"their":[90],"predictive":[91],"performance.":[92],"To":[93],"enhance":[94],"physics":[95],"consistency,":[96],"incorporate":[98],"physics-informed":[99],"loss":[100],"functions.":[101],"Our":[102],"results":[103],"demonstrate":[104],"that":[105],"FNO":[106,137],"outperforms":[107],"AE":[108],"achieving":[111],"mean":[113],"squared":[114],"error":[115],"(MSE)":[116],"as":[117,119],"low":[118],"0.0017":[120],"providing":[122],"speedups":[123],"up":[125],"to":[126,130,159,171],"1000":[127],"times":[128],"compared":[129],"CFD.":[131],"Additionally,":[132],"mesh-invariant":[134],"property":[135],"emphasizes":[138],"its":[139],"suitability":[140],"tasks,":[144],"where":[145],"varying":[146],"mesh":[147],"resolutions":[148],"required.":[150],"This":[151],"study":[152],"highlights":[153],"potential":[155],"machine":[157],"learning":[158],"accelerate":[160],"predictions":[163],"media,":[166],"offering":[167],"scalable":[169],"alternative":[170],"traditional":[172],"numerical":[173],"methods.":[174]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-03-11T00:00:00"}