{"id":"https://openalex.org/W7160357536","doi":"https://doi.org/10.48550/arxiv.2605.00937","title":"An ALE-Consistent Graph Neural Operator-Transformer Framework for Fluid-Structure Interaction","display_name":"An ALE-Consistent Graph Neural Operator-Transformer Framework for Fluid-Structure Interaction","publication_year":2026,"publication_date":"2026-05-01","ids":{"openalex":"https://openalex.org/W7160357536","doi":"https://doi.org/10.48550/arxiv.2605.00937"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.00937","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.00937","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":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.00937","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101693245","display_name":"Shihang Zhao","orcid":"https://orcid.org/0009-0008-2941-334X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhao, Shihang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073247162","display_name":"Mart\u00edn Saravia","orcid":"https://orcid.org/0000-0002-1558-8631"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Saravia, Mart\u00edn","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135294213","display_name":"Haokui Jiang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jiang, Haokui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135296452","display_name":"Zhiyang Xue","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xue, Zhiyang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5066899950","display_name":"Shunxiang Cao","orcid":"https://orcid.org/0000-0002-9000-9871"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cao, Shunxiang","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/T11206","display_name":"Model Reduction and Neural Networks","score":0.89410001039505,"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":0.89410001039505,"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/T11254","display_name":"Fluid Dynamics and Vibration Analysis","score":0.05770000070333481,"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/T11751","display_name":"Lattice Boltzmann Simulation Studies","score":0.023399999365210533,"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/kinematics","display_name":"Kinematics","score":0.7138000130653381},{"id":"https://openalex.org/keywords/extrapolation","display_name":"Extrapolation","score":0.6308000087738037},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.521399974822998},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.5120999813079834},{"id":"https://openalex.org/keywords/image-warping","display_name":"Image warping","score":0.48019999265670776},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.45399999618530273},{"id":"https://openalex.org/keywords/autoregressive-model","display_name":"Autoregressive model","score":0.42320001125335693}],"concepts":[{"id":"https://openalex.org/C39920418","wikidata":"https://www.wikidata.org/wiki/Q11476","display_name":"Kinematics","level":2,"score":0.7138000130653381},{"id":"https://openalex.org/C132459708","wikidata":"https://www.wikidata.org/wiki/Q744069","display_name":"Extrapolation","level":2,"score":0.6308000087738037},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6021000146865845},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.521399974822998},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.5120999813079834},{"id":"https://openalex.org/C157202957","wikidata":"https://www.wikidata.org/wiki/Q1659609","display_name":"Image warping","level":2,"score":0.48019999265670776},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.45399999618530273},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.453000009059906},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44130000472068787},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.42320001125335693},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.3720000088214874},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.35120001435279846},{"id":"https://openalex.org/C137800194","wikidata":"https://www.wikidata.org/wiki/Q11713455","display_name":"Interpolation (computer graphics)","level":3,"score":0.34369999170303345},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34279999136924744},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.30390000343322754},{"id":"https://openalex.org/C22367795","wikidata":"https://www.wikidata.org/wiki/Q7625208","display_name":"Structured prediction","level":2,"score":0.28299999237060547},{"id":"https://openalex.org/C187691185","wikidata":"https://www.wikidata.org/wiki/Q2020720","display_name":"Grid","level":2,"score":0.2694999873638153},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.25920000672340393},{"id":"https://openalex.org/C42536954","wikidata":"https://www.wikidata.org/wiki/Q7049462","display_name":"Nonlinear autoregressive exogenous model","level":3,"score":0.25690001249313354}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.00937","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.00937","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":"doi:10.48550/arxiv.2605.00937","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.00937","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":"Preprint"},"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":{"We":[0],"propose":[1],"an":[2,78],"arbitrary":[3],"Lagrangian-Eulerian":[4],"(ALE)-consistent":[5],"machine":[6],"learning":[7],"framework":[8,128],"for":[9,40],"long-term":[10,102,123,182],"fluid-structure":[11],"interaction":[12],"(FSI)":[13],"prediction":[14],"on":[15,131],"deforming":[16],"unstructured":[17],"meshes.":[18],"Specifically,":[19],"the":[20,55,72,84,89,132,141,171,175],"fluid":[21],"dynamics":[22],"are":[23,60],"modeled":[24],"by":[25,122],"a":[26,30,36,44,63,110,136,144],"surrogate":[27],"that":[28,82],"combines":[29],"graph":[31],"neural":[32],"operator":[33],"(GNO)":[34],"with":[35,88],"vision":[37],"Transformer":[38],"(ViT)":[39],"spatiotemporal":[41],"prediction,":[42],"while":[43],"lightweight":[45],"long":[46,152],"short-term":[47],"memory":[48],"(LSTM)":[49],"network":[50],"predicts":[51],"structural":[52,91],"kinematics":[53],"at":[54,71,93],"interface.":[56],"The":[57,126],"two":[58],"surrogates":[59],"coupled":[61],"through":[62],"standard":[64],"partitioned":[65],"procedure.":[66],"Most":[67],"importantly,":[68],"kinematic":[69],"compatibility":[70],"moving":[73],"interface":[74,86],"is":[75,114,129],"enforced":[76],"via":[77],"ALE-consistent":[79,178],"boundary-correction":[80],"step":[81],"updates":[83],"fluid-side":[85],"velocity":[87,92],"predicted":[90],"each":[94],"coupling":[95],"update,":[96],"thereby":[97],"improving":[98],"near-interface":[99],"accuracy":[100,186],"and":[101,154,163,181,187],"rollout":[103,188],"stability.":[104],"To":[105],"mitigate":[106],"autoregressive":[107,124],"error":[108],"accumulation,":[109],"two-stage":[111],"training":[112,183],"strategy":[113],"adopted,":[115],"consisting":[116],"of":[117,135,143,174],"single-step":[118],"supervised":[119],"pretraining":[120],"followed":[121],"fine-tuning.":[125],"proposed":[127],"validated":[130],"benchmark":[133],"problem":[134],"flexible":[137],"beam":[138],"vibration":[139],"in":[140,160],"wake":[142],"cylinder.":[145],"Results":[146],"demonstrate":[147],"accurate":[148],"phase-consistent":[149],"predictions":[150],"over":[151],"rollouts":[153],"robust":[155],"generalization":[156],"under":[157],"inlet-profile":[158],"variations":[159],"both":[161],"interpolation":[162],"extrapolation":[164],"settings.":[165],"Systematic":[166],"ablation":[167],"studies":[168],"further":[169],"assess":[170],"respective":[172],"contributions":[173],"ViT":[176],"module,":[177],"boundary":[179],"correction,":[180],"to":[184],"predictive":[185],"robustness.":[189]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-06T00:00:00"}
