{"id":"https://openalex.org/W7151901436","doi":"https://doi.org/10.48550/arxiv.2604.05652","title":"Multiscale Physics-Informed Neural Network for Complex Fluid Flows with Long-Range Dependencies","display_name":"Multiscale Physics-Informed Neural Network for Complex Fluid Flows with Long-Range Dependencies","publication_year":2026,"publication_date":"2026-04-07","ids":{"openalex":"https://openalex.org/W7151901436","doi":"https://doi.org/10.48550/arxiv.2604.05652"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.05652","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.05652","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":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.05652","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5133209678","display_name":"Prashant Kumar","orcid":"https://orcid.org/0009-0007-0359-9468"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kumar, Prashant","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5133153858","display_name":"Rajesh Ranjan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ranjan, Rajesh","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"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/T11206","display_name":"Model Reduction and Neural Networks","score":0.9911999702453613,"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":0.9911999702453613,"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/T11751","display_name":"Lattice Boltzmann Simulation Studies","score":0.0010999999940395355,"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"}},{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.0008999999845400453,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/nonlinear-system","display_name":"Nonlinear system","score":0.6633999943733215},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6233000159263611},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5777000188827515},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.5519999861717224},{"id":"https://openalex.org/keywords/fluid-dynamics","display_name":"Fluid dynamics","score":0.5080000162124634},{"id":"https://openalex.org/keywords/boundary","display_name":"Boundary (topology)","score":0.4666999876499176},{"id":"https://openalex.org/keywords/turbulence","display_name":"Turbulence","score":0.42739999294281006}],"concepts":[{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.6633999943733215},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6233000159263611},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5920000076293945},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5777000188827515},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.5519999861717224},{"id":"https://openalex.org/C90278072","wikidata":"https://www.wikidata.org/wiki/Q216320","display_name":"Fluid dynamics","level":2,"score":0.5080000162124634},{"id":"https://openalex.org/C62354387","wikidata":"https://www.wikidata.org/wiki/Q875399","display_name":"Boundary (topology)","level":2,"score":0.4666999876499176},{"id":"https://openalex.org/C196558001","wikidata":"https://www.wikidata.org/wiki/Q190132","display_name":"Turbulence","level":2,"score":0.42739999294281006},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4018999934196472},{"id":"https://openalex.org/C76563973","wikidata":"https://www.wikidata.org/wiki/Q189452","display_name":"Laminar flow","level":2,"score":0.4011000096797943},{"id":"https://openalex.org/C38349280","wikidata":"https://www.wikidata.org/wiki/Q1434290","display_name":"Flow (mathematics)","level":2,"score":0.38989999890327454},{"id":"https://openalex.org/C1633027","wikidata":"https://www.wikidata.org/wiki/Q815820","display_name":"Computational fluid dynamics","level":2,"score":0.3605000078678131},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3449999988079071},{"id":"https://openalex.org/C47822265","wikidata":"https://www.wikidata.org/wiki/Q854457","display_name":"Complex system","level":2,"score":0.32429999113082886},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.323199987411499},{"id":"https://openalex.org/C182310444","wikidata":"https://www.wikidata.org/wiki/Q1332643","display_name":"Boundary value problem","level":2,"score":0.31139999628067017},{"id":"https://openalex.org/C111603439","wikidata":"https://www.wikidata.org/wiki/Q752193","display_name":"Boundary layer","level":2,"score":0.3109999895095825},{"id":"https://openalex.org/C93779851","wikidata":"https://www.wikidata.org/wiki/Q271977","display_name":"Partial differential equation","level":2,"score":0.2728999853134155},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.27160000801086426},{"id":"https://openalex.org/C141123601","wikidata":"https://www.wikidata.org/wiki/Q6935072","display_name":"Multiscale modeling","level":2,"score":0.2567000091075897}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.05652","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.05652","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":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.05652","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.05652","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":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":{"Fluid":[0],"flows":[1,223],"are":[2,45],"governed":[3],"by":[4,47],"the":[5,68,107,122,139,152,163,185,201,218],"nonlinear":[6,123],"Navier-Stokes":[7,128],"equations,":[8],"which":[9,56],"can":[10],"manifest":[11],"multiscale":[12,82,118],"dynamics":[13,160],"even":[14],"from":[15,52,224],"predictable":[16],"initial":[17],"conditions.":[18],"Predicting":[19],"such":[20,81],"phenomena":[21],"remains":[22],"a":[23,76,92,112,117],"formidable":[24],"challenge":[25],"in":[26,210],"scientific":[27],"machine":[28],"learning,":[29],"particularly":[30],"regarding":[31],"convergence":[32,188],"speed,":[33],"data":[34,61],"requirements,":[35],"and":[36,70,126,174],"solution":[37],"accuracy.":[38,211],"In":[39],"complex":[40,221],"fluid":[41,159],"flows,":[42],"these":[43],"challenges":[44],"exacerbated":[46],"long-range":[48],"spatial":[49],"dependencies":[50,99],"arising":[51],"distant":[53],"boundary":[54,132,170],"conditions,":[55],"typically":[57],"necessitate":[58],"extensive":[59],"supervision":[60,195],"to":[62,79,138,157,189],"achieve":[63],"acceptable":[64],"results.":[65],"We":[66],"propose":[67],"Domain-Decomposed":[69],"Shifted":[71],"Physics-Informed":[72],"Neural":[73],"Network":[74],"(DDS-PINN),":[75],"framework":[77,186],"designed":[78],"resolve":[80],"interactions":[83],"with":[84,91],"minimal":[85],"supervision.":[86],"By":[87],"utilizing":[88],"localized":[89],"networks":[90],"unified":[93],"global":[94,98],"loss,":[95],"DDS-PINN":[96,135],"captures":[97],"while":[100],"maintaining":[101],"local":[102],"precision.":[103],"The":[104],"robustness":[105],"of":[106,114,130,200,220],"approach":[108,213],"is":[109,136],"demonstrated":[110],"across":[111],"suite":[113],"benchmarks,":[115],"including":[116],"linear":[119],"differential":[120],"equation,":[121,125],"Burgers'":[124],"data-free":[127],"simulations":[129],"flat-plate":[131],"layers.":[133],"Finally,":[134],"applied":[137],"computationally":[140],"challenging":[141],"backward-facing":[142],"step":[143],"(BFS)":[144],"problem;":[145],"for":[146,165,217],"laminar":[147],"regimes":[148],"(Re":[149],"=":[150,183],"100),":[151],"model":[153],"yields":[154],"results":[155],"comparable":[156],"computational":[158],"(CFD)":[161],"without":[162],"need":[164],"any":[166],"data,":[167],"accurately":[168],"predicting":[169],"layer":[171],"thickness,":[172],"separation,":[173],"reattachment":[175],"lengths.":[176],"For":[177],"turbulent":[178,222],"BFS":[179],"flow":[180],"at":[181],"Re":[182],"10,000,":[184],"achieves":[187],"O(10^-4)":[190],"using":[191],"only":[192],"500":[193],"random":[194],"points":[196],"(&lt;":[197],"0.3":[198],"%":[199],"total":[202],"domain),":[203],"outperforming":[204],"established":[205],"methods":[206],"like":[207],"Residual-based":[208],"Attention-PINN":[209],"This":[212],"demonstrates":[214],"strong":[215],"potential":[216],"super-resolution":[219],"sparse":[225],"experimental":[226],"measurements.":[227]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-04-09T00:00:00"}
