{"id":"https://openalex.org/W7130386446","doi":"https://doi.org/10.48550/arxiv.2602.15632","title":"Neural-POD: A Plug-and-Play Neural Operator Framework for Infinite-Dimensional Functional Nonlinear Proper Orthogonal Decomposition","display_name":"Neural-POD: A Plug-and-Play Neural Operator Framework for Infinite-Dimensional Functional Nonlinear Proper Orthogonal Decomposition","publication_year":2026,"publication_date":"2026-02-17","ids":{"openalex":"https://openalex.org/W7130386446","doi":"https://doi.org/10.48550/arxiv.2602.15632"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2602.15632","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.15632","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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.2602.15632","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5082351647","display_name":"Changhong Mou","orcid":"https://orcid.org/0009-0009-7045-0668"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Mou, Changhong","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025474821","display_name":"Binghang Lu","orcid":"https://orcid.org/0009-0001-6001-6632"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lu, Binghang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5126308305","display_name":"Guang Lin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lin, Guang","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5082351647"],"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":0.9976000189781189,"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":0.9976000189781189,"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/T11948","display_name":"Machine Learning in Materials Science","score":0.0005000000237487257,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"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":0.00019999999494757503,"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/generalization","display_name":"Generalization","score":0.650600016117096},{"id":"https://openalex.org/keywords/nonlinear-system","display_name":"Nonlinear system","score":0.5867999792098999},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5759999752044678},{"id":"https://openalex.org/keywords/operator","display_name":"Operator (biology)","score":0.5242000222206116},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4722000062465668},{"id":"https://openalex.org/keywords/transfer-function","display_name":"Transfer function","score":0.4690999984741211},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.46140000224113464},{"id":"https://openalex.org/keywords/orthogonal-functions","display_name":"Orthogonal functions","score":0.45500001311302185}],"concepts":[{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.650600016117096},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.5867999792098999},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5759999752044678},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5264000296592712},{"id":"https://openalex.org/C17020691","wikidata":"https://www.wikidata.org/wiki/Q139677","display_name":"Operator (biology)","level":5,"score":0.5242000222206116},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4722000062465668},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46950000524520874},{"id":"https://openalex.org/C81299745","wikidata":"https://www.wikidata.org/wiki/Q334269","display_name":"Transfer function","level":2,"score":0.4690999984741211},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.46709999442100525},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.46140000224113464},{"id":"https://openalex.org/C135925592","wikidata":"https://www.wikidata.org/wiki/Q2637908","display_name":"Orthogonal functions","level":2,"score":0.45500001311302185},{"id":"https://openalex.org/C124681953","wikidata":"https://www.wikidata.org/wiki/Q339062","display_name":"Decomposition","level":2,"score":0.4496000111103058},{"id":"https://openalex.org/C5917680","wikidata":"https://www.wikidata.org/wiki/Q2621825","display_name":"Basis function","level":2,"score":0.44859999418258667},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.41290000081062317},{"id":"https://openalex.org/C49766605","wikidata":"https://www.wikidata.org/wiki/Q207643","display_name":"Linear map","level":2,"score":0.39899998903274536},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.397599995136261},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.38580000400543213},{"id":"https://openalex.org/C12426560","wikidata":"https://www.wikidata.org/wiki/Q189569","display_name":"Basis (linear algebra)","level":2,"score":0.38040000200271606},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.36660000681877136},{"id":"https://openalex.org/C187064257","wikidata":"https://www.wikidata.org/wiki/Q3306808","display_name":"Orthogonal basis","level":2,"score":0.31279999017715454},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.2720000147819519},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.2712000012397766},{"id":"https://openalex.org/C54940322","wikidata":"https://www.wikidata.org/wiki/Q3997740","display_name":"Orthogonal transformation","level":2,"score":0.25589999556541443},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.25270000100135803},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.25209999084472656},{"id":"https://openalex.org/C22789450","wikidata":"https://www.wikidata.org/wiki/Q420904","display_name":"Singular value decomposition","level":2,"score":0.25110000371932983}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2602.15632","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.15632","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2602.15632","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.15632","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"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":{"AI":[0],"for":[1,119],"science":[2],"(AI4Science)":[3],"models":[4,54],"often":[5],"suffer":[6],"from":[7],"discretization:":[8],"learned":[9,71,106],"representations":[10],"remain":[11],"tied":[12],"to":[13,77,93],"the":[14,105],"training":[15,83],"grid,":[16],"limiting":[17],"transfer":[18],"across":[19],"resolutions,":[20],"solvers":[21],"and":[22,45,55,97,110,127],"applications.":[23],"We":[24,122],"introduce":[25],"Neural":[26],"Proper":[27],"Orthogonal":[28],"Decomposition":[29],"(Neural-POD),":[30],"a":[31,115],"plug-and-play":[32],"neural":[33],"operator":[34],"that":[35],"learns":[36],"nonlinear,":[37],"orthogonal":[38],"basis":[39],"functions":[40],"directly":[41],"in":[42,49,101],"function":[43],"space":[44],"can":[46],"be":[47],"integrated":[48],"both":[50],"projection-based":[51],"reduced":[52],"order":[53],"operator-learning":[56],"frameworks":[57],"such":[58],"as":[59,114],"DeepONet.":[60],"Neural-POD":[61,112,124],"replaces":[62],"SVD-derived,":[63],"resolution-dependent":[64],"linear":[65],"modes":[66],"with":[67],"continuous,":[68],"resolution-invariant":[69],"bases":[70,107],"via":[72],"sequential":[73],"residual":[74],"minimization,":[75],"analogous":[76],"Gram-Schmidt":[78],"orthogonalization.":[79],"The":[80],"framework":[81],"supports":[82],"under":[84],"task-specific":[85],"norms":[86],"(e.g.,":[87],"$L^2$,":[88],"$L^1$),":[89],"improves":[90],"out-of-distribution":[91],"generalization":[92],"unseen":[94],"parameter":[95],"regimes,":[96],"captures":[98],"nonlinear":[99],"structure":[100],"complex":[102],"systems.":[103],"Because":[104],"are":[108],"interpretable":[109],"reusable,":[111],"serves":[113],"general":[116],"representation":[117],"module":[118],"AI4Science":[120],"workflows.":[121],"demonstrate":[123],"on":[125],"Burgers'":[126],"Navier-Stokes":[128],"equations.":[129]},"counts_by_year":[],"updated_date":"2026-03-04T07:04:00.330322","created_date":"2026-02-19T00:00:00"}
