{"id":"https://openalex.org/W7161029252","doi":"https://doi.org/10.48550/arxiv.2605.11460","title":"Beyond Prediction: Interval Neural Networks for Uncertainty-Aware System Identification","display_name":"Beyond Prediction: Interval Neural Networks for Uncertainty-Aware System Identification","publication_year":2026,"publication_date":"2026-05-12","ids":{"openalex":"https://openalex.org/W7161029252","doi":"https://doi.org/10.48550/arxiv.2605.11460"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.11460","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.11460","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.2605.11460","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5118356656","display_name":"Mehmet Ali Ferah","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ferah, Mehmet Ali","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5010725194","display_name":"Tufan Kumbasar","orcid":"https://orcid.org/0000-0001-9366-0240"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kumbasar, Tufan","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":0.6272000074386597,"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":0.6272000074386597,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.09200000017881393,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11236","display_name":"Control Systems and Identification","score":0.05820000171661377,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"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/interval","display_name":"Interval (graph theory)","score":0.6104000210762024},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.6032999753952026},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6029999852180481},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5450000166893005},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.5315999984741211},{"id":"https://openalex.org/keywords/interval-arithmetic","display_name":"Interval arithmetic","score":0.5238000154495239},{"id":"https://openalex.org/keywords/uncertainty-quantification","display_name":"Uncertainty quantification","score":0.42809998989105225},{"id":"https://openalex.org/keywords/system-identification","display_name":"System identification","score":0.41029998660087585}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6947000026702881},{"id":"https://openalex.org/C2778067643","wikidata":"https://www.wikidata.org/wiki/Q166507","display_name":"Interval (graph theory)","level":2,"score":0.6104000210762024},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.6032999753952026},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6029999852180481},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5947999954223633},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5461000204086304},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5450000166893005},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.5315999984741211},{"id":"https://openalex.org/C191252586","wikidata":"https://www.wikidata.org/wiki/Q1671453","display_name":"Interval arithmetic","level":3,"score":0.5238000154495239},{"id":"https://openalex.org/C32230216","wikidata":"https://www.wikidata.org/wiki/Q7882499","display_name":"Uncertainty quantification","level":2,"score":0.42809998989105225},{"id":"https://openalex.org/C119247159","wikidata":"https://www.wikidata.org/wiki/Q1366192","display_name":"System identification","level":3,"score":0.41029998660087585},{"id":"https://openalex.org/C103402496","wikidata":"https://www.wikidata.org/wiki/Q1106171","display_name":"Prediction interval","level":2,"score":0.39329999685287476},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.37070000171661377},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.35030001401901245},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3492000102996826},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.3018999993801117},{"id":"https://openalex.org/C22157029","wikidata":"https://www.wikidata.org/wiki/Q17080460","display_name":"Nonlinear system identification","level":4,"score":0.288100004196167},{"id":"https://openalex.org/C34146451","wikidata":"https://www.wikidata.org/wiki/Q5048094","display_name":"Cascade","level":2,"score":0.2872999906539917},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.2800999879837036},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2759999930858612},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.2685999870300293},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.2567000091075897}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.11460","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.11460","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.2605.11460","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.11460","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":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"System":[0],"identification":[1],"(SysID)":[2],"is":[3,34,179,189,192],"critical":[4],"for":[5,27,46,54],"modeling":[6,28],"dynamical":[7],"systems":[8],"from":[9],"experimental":[10],"data,":[11],"yet":[12],"traditional":[13],"approaches":[14,150],"often":[15],"fail":[16],"to":[17,36,85,135,175,194],"capture":[18],"nonlinear":[19],"behaviors.":[20],"While":[21],"deep":[22,215],"learning":[23,216],"offers":[24],"powerful":[25],"tools":[26],"such":[29],"dynamics,":[30],"incorporating":[31],"uncertainty":[32,74,87,178],"quantification":[33],"essential":[35],"ensure":[37,136],"reliable":[38,137],"predictions.":[39],"This":[40,81],"paper":[41],"presents":[42],"a":[43,101,105,116],"systematic":[44],"framework":[45,118,212],"constructing":[47],"and":[48,69,88,112,123,132,151,170],"training":[49,201],"interval":[50,63,76,124],"Neural":[51],"Networks":[52],"(INNs)":[53],"uncertainty-aware":[55,129,157,218],"SysID.":[56],"By":[57],"extending":[58],"crisp":[59,107],"neural":[60],"networks":[61],"into":[62,109],"counterparts,":[64],"we":[65,94],"develop":[66],"Interval":[67],"LSTM":[68],"NODE":[70],"models":[71],"that":[72,209],"propagate":[73],"through":[75],"arithmetic":[77],"without":[78],"probabilistic":[79],"assumptions.":[80],"design":[82],"allows":[83],"them":[84],"represent":[86],"produce":[89],"prediction":[90,121,163,172],"intervals.":[91,173],"For":[92],"training,":[93],"propose":[95],"two":[96,200],"strategies:":[97],"Cascade":[98],"INN":[99,114],"(C-INN),":[100],"two-stage":[102],"approach":[103],"converting":[104],"trained":[106],"NN":[108],"an":[110],"INN,":[111],"Joint":[113],"(J-INN),":[115],"one-stage":[117],"jointly":[119],"optimizing":[120],"accuracy":[122],"precision.":[125],"Both":[126],"strategies":[127],"employ":[128],"loss":[130],"functions":[131],"parameterization":[133],"tricks":[134],"learning.":[138],"Comprehensive":[139],"experiments":[140],"on":[141],"multiple":[142],"SysID":[143],"datasets":[144],"demonstrate":[145,208],"the":[146,184,199,210],"effectiveness":[147],"of":[148,186,205],"both":[149],"benchmark":[152],"their":[153],"performance":[154],"against":[155],"well-established":[156],"baselines:":[158],"C-INN":[159],"achieves":[160],"superior":[161],"point":[162],"accuracy,":[164],"whereas":[165],"J-INN":[166],"yields":[167],"more":[168],"accurate":[169],"better-calibrated":[171],"Furthermore,":[174],"reveal":[176],"how":[177],"represented":[180],"across":[181,198],"model":[182],"parameters,":[183],"concept":[185],"channel-wise":[187],"elasticity":[188],"introduced,":[190],"which":[191],"used":[193],"identify":[195],"distinct":[196],"patterns":[197],"strategies.":[202],"The":[203],"results":[204],"this":[206],"study":[207],"proposed":[211],"effectively":[213],"integrates":[214],"with":[217],"modeling.":[219]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-05-14T00:00:00"}