{"id":"https://openalex.org/W7161678702","doi":"https://doi.org/10.48550/arxiv.2605.18188","title":"UTOPYA: A Multimodal Deep Learning Framework for Physics-Informed Anomaly Detection and Time-Series Prediction","display_name":"UTOPYA: A Multimodal Deep Learning Framework for Physics-Informed Anomaly Detection and Time-Series Prediction","publication_year":2026,"publication_date":"2026-05-18","ids":{"openalex":"https://openalex.org/W7161678702","doi":"https://doi.org/10.48550/arxiv.2605.18188"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.18188","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.18188","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.2605.18188","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5136499181","display_name":"Robson W. S. Pessoa","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pessoa, Robson W. S.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5092533132","display_name":"Julien Amblard","orcid":"https://orcid.org/0000-0002-7072-9281"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Amblard, Julien","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136475233","display_name":"Alessandra Russo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Russo, Alessandra","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136463339","display_name":"Idelfonso B. R. Nogueira","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nogueira, Idelfonso B. R.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"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/T10876","display_name":"Fault Detection and Control Systems","score":0.7479000091552734,"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"}},"topics":[{"id":"https://openalex.org/T10876","display_name":"Fault Detection and Control Systems","score":0.7479000091552734,"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"}},{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.1476999968290329,"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/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.02160000056028366,"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/anomaly-detection","display_name":"Anomaly detection","score":0.75},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5806000232696533},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5591999888420105},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.5482000112533569},{"id":"https://openalex.org/keywords/smoothness","display_name":"Smoothness","score":0.45509999990463257},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.39969998598098755},{"id":"https://openalex.org/keywords/distillation","display_name":"Distillation","score":0.3824999928474426},{"id":"https://openalex.org/keywords/fault-detection-and-isolation","display_name":"Fault detection and isolation","score":0.3686999976634979}],"concepts":[{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.75},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6876000165939331},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6359000205993652},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5806000232696533},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5591999888420105},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.5482000112533569},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.48750001192092896},{"id":"https://openalex.org/C102634674","wikidata":"https://www.wikidata.org/wiki/Q868473","display_name":"Smoothness","level":2,"score":0.45509999990463257},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.39969998598098755},{"id":"https://openalex.org/C204030448","wikidata":"https://www.wikidata.org/wiki/Q101017","display_name":"Distillation","level":2,"score":0.3824999928474426},{"id":"https://openalex.org/C152745839","wikidata":"https://www.wikidata.org/wiki/Q5438153","display_name":"Fault detection and isolation","level":3,"score":0.3686999976634979},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3650999963283539},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.3634999990463257},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.35260000824928284},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.32600000500679016},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.3059000074863434},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.30320000648498535},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2962000072002411},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.2791999876499176},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.27469998598098755},{"id":"https://openalex.org/C16910744","wikidata":"https://www.wikidata.org/wiki/Q7705759","display_name":"Test data","level":2,"score":0.2653000056743622},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.2603999972343445},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2515999972820282},{"id":"https://openalex.org/C175551986","wikidata":"https://www.wikidata.org/wiki/Q47089","display_name":"Fault (geology)","level":2,"score":0.2515000104904175}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.18188","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.18188","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.2605.18188","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.18188","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":{"Anomaly":[0,28],"detection":[1],"in":[2,47,71,86,201],"batch":[3,48,95],"processes":[4],"is":[5,153],"hindered":[6],"by":[7,50,161],"transient":[8],"dynamics,":[9],"scarce":[10],"fault":[11],"labels,":[12],"and":[13,30,44,63,77,111,126,190,215],"reliance":[14],"on":[15],"single-modality":[16],"sensor":[17],"data.":[18],"This":[19],"work":[20,73],"introduces":[21,83],"UTOPYA":[22,103],"(Unified":[23],"Temporal":[24],"Observation":[25],"for":[26,221],"Physics-Informed":[27],"Detection":[29],"Time-Series":[31],"Prediction),":[32],"a":[33,105,171,209],"15.2M-parameter":[34],"multimodal":[35,94,141,222],"framework":[36],"that":[37,147,179],"jointly":[38],"addresses":[39],"anomaly":[40,216],"detection,":[41,217],"time-series":[42],"prediction,":[43],"phase":[45],"classification":[46],"distillation":[49,96],"fusing":[51],"eight":[52],"data":[53],"modalities":[54],"through":[55],"Feature-wise":[56],"Linear":[57],"Modulation":[58],"(FiLM)":[59],"conditioned":[60],"cross-modal":[61],"attention":[62],"gated":[64],"fusion.":[65],"A":[66,140],"physics-informed":[67],"regularisation":[68,214],"scheme":[69],"introduced":[70],"this":[72,202],"enforces":[74],"temporal":[75],"smoothness":[76],"thermodynamic":[78],"monotonicity,":[79],"while":[80],"curriculum":[81],"learning":[82],"training":[84,172],"samples":[85],"order":[87],"of":[88,98,109],"physical":[89],"difficulty.":[90],"On":[91],"the":[92,137,154,164],"119-experiment":[93],"dataset":[97],"Arweiler":[99],"et":[100],"al.":[101],"(2026),":[102],"achieves":[104],"window-level":[106,134],"test":[107],"AUROC":[108,135,160],"0.832":[110],"0.874":[112],"under":[113,130],"multi-signal":[114,159],"experiment-level":[115,158],"scoring,":[116],"substantially":[117],"outperforming":[118],"four":[119],"external":[120],"baselines":[121],"(PCA,":[122],"autoencoder,":[123],"Isolation":[124],"Forest,":[125],"LSTM":[127],"autoencoder)":[128],"evaluated":[129],"identical":[131],"conditions":[132],"(+0.147":[133],"over":[136,143,163],"best":[138],"baseline).":[139],"ablation":[142,173],"15~architectural":[144],"configurations":[145],"shows":[146],"static":[148],"context":[149],"via":[150],"FiLM":[151],"conditioning":[152],"key":[155],"enabler,":[156],"lifting":[157],"+0.145":[162],"unimodal":[165],"baseline":[166],"(0.729":[167],"to":[168,195],"0.874).":[169],"Separately,":[170],"across":[174],"14":[175],"design":[176],"choices":[177],"reveals":[178],"several":[180],"widely-adopted":[181],"techniques,":[182],"including":[183],"instance":[184],"normalisation,":[185],"Mixup,":[186],"ensembling,":[187],"test-time":[188],"augmentation,":[189],"stochastic":[191],"weight":[192],"averaging,":[193],"fail":[194],"improve":[196],"or":[197],"actively":[198],"degrade":[199],"generalisation":[200],"data-scarce":[203],"setting.":[204],"These":[205],"negative":[206],"results":[207],"expose":[208],"fundamental":[210],"tension":[211],"between":[212],"smoothing-based":[213],"providing":[218],"practical":[219],"guidance":[220],"process":[223],"monitoring":[224],"deployment.":[225]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-05-20T00:00:00"}