{"id":"https://openalex.org/W7205322648","doi":"https://doi.org/10.55037/lxlaser.22nd.30","title":"A lean probabilistic CNN for PIV enabling joint flow and reconstruction uncertainty estimation","display_name":"A lean probabilistic CNN for PIV enabling joint flow and reconstruction uncertainty estimation","publication_year":2026,"publication_date":"2026-07-01","ids":{"openalex":"https://openalex.org/W7205322648","doi":"https://doi.org/10.55037/lxlaser.22nd.30"},"language":"en","primary_location":{"id":"doi:10.55037/lxlaser.22nd.30","doi":"https://doi.org/10.55037/lxlaser.22nd.30","is_oa":false,"landing_page_url":"https://doi.org/10.55037/lxlaser.22nd.30","pdf_url":null,"source":{"id":"https://openalex.org/S7407097107","display_name":"International Symposium on the Application of Laser and Imaging Techniques to Fluid Mechanics (Lisbon Laser Symposium)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 22nd International Symposium on the Application of Laser and Imaging Techniques to Fluid Mechanics","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://www.dora.lib4ri.ch/empa/dload/empa:46700/PDF/view","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5078880549","display_name":"Claudio Mucignat","orcid":"https://orcid.org/0000-0003-2676-2815","observed_orcids":["https://orcid.org/0000-0003-2676-2815"]},"institutions":[{"id":"https://openalex.org/I71824836","display_name":"Empa","ror":"https://ror.org/02x681a42","country_code":"CH","type":"facility","lineage":["https://openalex.org/I2799323385","https://openalex.org/I71824836"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"C. Mucignat","raw_affiliation_strings":["Laboratory for Computational Engineering, Swiss Federal Laboratories for Materials Science and Technology, Switzerland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Laboratory for Computational Engineering, Swiss Federal Laboratories for Materials Science and Technology, Switzerland","institution_ids":["https://openalex.org/I71824836"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018932973","display_name":"Kamila Zdyba\u0142","orcid":"https://orcid.org/0000-0002-3952-3824","observed_orcids":["https://orcid.org/0000-0002-3952-3824"]},"institutions":[{"id":"https://openalex.org/I71824836","display_name":"Empa","ror":"https://ror.org/02x681a42","country_code":"CH","type":"facility","lineage":["https://openalex.org/I2799323385","https://openalex.org/I71824836"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"K. Zydbal","raw_affiliation_strings":["Laboratory for Computational Engineering, Swiss Federal Laboratories for Materials Science and Technology, Switzerland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Laboratory for Computational Engineering, Swiss Federal Laboratories for Materials Science and Technology, Switzerland","institution_ids":["https://openalex.org/I71824836"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132983132","display_name":"H. Gorji","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I71824836","display_name":"Empa","ror":"https://ror.org/02x681a42","country_code":"CH","type":"facility","lineage":["https://openalex.org/I2799323385","https://openalex.org/I71824836"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"H. Gorji","raw_affiliation_strings":["Laboratory for Computational Engineering, Swiss Federal Laboratories for Materials Science and Technology, Switzerland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Laboratory for Computational Engineering, Swiss Federal Laboratories for Materials Science and Technology, Switzerland","institution_ids":["https://openalex.org/I71824836"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5058591095","display_name":"Ivan Lunati","orcid":"https://orcid.org/0000-0002-3205-7429","observed_orcids":["https://orcid.org/0000-0002-3205-7429"]},"institutions":[{"id":"https://openalex.org/I71824836","display_name":"Empa","ror":"https://ror.org/02x681a42","country_code":"CH","type":"facility","lineage":["https://openalex.org/I2799323385","https://openalex.org/I71824836"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"I. Lunati","raw_affiliation_strings":["Laboratory for Computational Engineering, Swiss Federal Laboratories for Materials Science and Technology, Switzerland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Laboratory for Computational Engineering, Swiss Federal Laboratories for Materials Science and Technology, Switzerland","institution_ids":["https://openalex.org/I71824836"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I71824836"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"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/T10360","display_name":"Fluid Dynamics and Turbulent Flows","score":0.8252000212669373,"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"}},"topics":[{"id":"https://openalex.org/T10360","display_name":"Fluid Dynamics and Turbulent Flows","score":0.8252000212669373,"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/T11206","display_name":"Model Reduction and Neural Networks","score":0.13899999856948853,"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/T10531","display_name":"Advanced Vision and Imaging","score":0.0032999999821186066,"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/particle-image-velocimetry","display_name":"particle image velocimetry","score":0.9729999899864197},{"id":"https://openalex.org/keywords/probabilistic-neural-network","display_name":"probabilistic neural network","score":0.6890000104904175},{"id":"https://openalex.org/keywords/uncertainty-estimation","display_name":"uncertainty estimation","score":0.7200000286102295},{"id":"https://openalex.org/keywords/aleatoric-uncertainty","display_name":"aleatoric uncertainty","score":0.8970000147819519},{"id":"https://openalex.org/keywords/displacement-field-reconstruction","display_name":"displacement field reconstruction","score":0.8410000205039978},{"id":"https://openalex.org/keywords/student-s-t-distribution","display_name":"t-distribution","score":0.8820000290870667},{"id":"https://openalex.org/keywords/uncertainty-calibration","display_name":"uncertainty calibration","score":0.6729999780654907},{"id":"https://openalex.org/keywords/flow-reconstruction","display_name":"flow reconstruction","score":0.453000009059906}],"concepts":[{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.60589998960495},{"id":"https://openalex.org/C32230216","wikidata":"https://www.wikidata.org/wiki/Q7882499","display_name":"Uncertainty quantification","level":2,"score":0.5781999826431274},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5544000267982483},{"id":"https://openalex.org/C207857233","wikidata":"https://www.wikidata.org/wiki/Q1756248","display_name":"Particle image velocimetry","level":3,"score":0.542900025844574},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.48969998955726624},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.48019999265670776},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4747999906539917},{"id":"https://openalex.org/C137209882","wikidata":"https://www.wikidata.org/wiki/Q1403517","display_name":"Measurement uncertainty","level":2,"score":0.42989999055862427},{"id":"https://openalex.org/C107551265","wikidata":"https://www.wikidata.org/wiki/Q1458245","display_name":"Displacement (psychology)","level":2,"score":0.41519999504089355},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.4075999855995178},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.4058000147342682},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.37880000472068787},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.3598000109195709},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3497999906539917},{"id":"https://openalex.org/C101104100","wikidata":"https://www.wikidata.org/wiki/Q1063540","display_name":"Heteroscedasticity","level":2,"score":0.34209999442100525},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.3255000114440918},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.3206000030040741},{"id":"https://openalex.org/C177803969","wikidata":"https://www.wikidata.org/wiki/Q29205","display_name":"Uncertainty analysis","level":2,"score":0.30570000410079956},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.30410000681877136},{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.29899999499320984},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.296099990606308},{"id":"https://openalex.org/C29660869","wikidata":"https://www.wikidata.org/wiki/Q5282615","display_name":"Displacement field","level":3,"score":0.29429998993873596},{"id":"https://openalex.org/C19619285","wikidata":"https://www.wikidata.org/wiki/Q196372","display_name":"Observational error","level":2,"score":0.2913999855518341},{"id":"https://openalex.org/C38349280","wikidata":"https://www.wikidata.org/wiki/Q1434290","display_name":"Flow (mathematics)","level":2,"score":0.28619998693466187},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.2854999899864197},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.273499995470047},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.27230000495910645},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.2639000117778778},{"id":"https://openalex.org/C134261354","wikidata":"https://www.wikidata.org/wiki/Q938438","display_name":"Statistical inference","level":2,"score":0.2596000134944916},{"id":"https://openalex.org/C55037315","wikidata":"https://www.wikidata.org/wiki/Q5421151","display_name":"Experimental data","level":2,"score":0.25440001487731934},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.25440001487731934},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.2513999938964844}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.55037/lxlaser.22nd.30","doi":"https://doi.org/10.55037/lxlaser.22nd.30","is_oa":false,"landing_page_url":"https://doi.org/10.55037/lxlaser.22nd.30","pdf_url":null,"source":{"id":"https://openalex.org/S7407097107","display_name":"International Symposium on the Application of Laser and Imaging Techniques to Fluid Mechanics (Lisbon Laser Symposium)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 22nd International Symposium on the Application of Laser and Imaging Techniques to Fluid Mechanics","raw_type":"proceedings-article"},{"id":"pmh:oai:dora:empa_46700","doi":null,"is_oa":true,"landing_page_url":"https://www.dora.lib4ri.ch/empa/item/empa:46700","pdf_url":"https://www.dora.lib4ri.ch/empa/dload/empa:46700/PDF/view","source":{"id":"https://openalex.org/S4306401298","display_name":"DORA Empa (Swiss Federal Laboratories for Materials Science and Technology (Empa))","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I71824836","host_organization_name":"Empa","host_organization_lineage":["https://openalex.org/I71824836"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Proceedings of the 22nd international symposium on application of laser and imaging techniques to fluid mechanics 2026","raw_type":"Proceedings Paper"}],"best_oa_location":{"id":"pmh:oai:dora:empa_46700","doi":null,"is_oa":true,"landing_page_url":"https://www.dora.lib4ri.ch/empa/item/empa:46700","pdf_url":"https://www.dora.lib4ri.ch/empa/dload/empa:46700/PDF/view","source":{"id":"https://openalex.org/S4306401298","display_name":"DORA Empa (Swiss Federal Laboratories for Materials Science and Technology (Empa))","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I71824836","host_organization_name":"Empa","host_organization_lineage":["https://openalex.org/I71824836"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Proceedings of the 22nd international symposium on application of laser and imaging techniques to fluid mechanics 2026","raw_type":"Proceedings Paper"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7205322648.pdf","grobid_xml":"https://content.openalex.org/works/W7205322648.grobid-xml"},"referenced_works_count":6,"referenced_works":["https://openalex.org/W2070200434","https://openalex.org/W2600383743","https://openalex.org/W2774438959","https://openalex.org/W3110381706","https://openalex.org/W4414781472","https://openalex.org/W6966805667"],"related_works":["https://openalex.org/W3110381706","https://openalex.org/W3108250699","https://openalex.org/W6966805667","https://openalex.org/W3203844758","https://openalex.org/W2095464851","https://openalex.org/W2070200434","https://openalex.org/W183031376","https://openalex.org/W4414781472","https://openalex.org/W2511369178","https://openalex.org/W6926659886"],"abstract_inverted_index":{"Particle":[0],"Image":[1],"Velocimetry":[2],"(PIV)":[3],"is":[4,147],"affected":[5],"by":[6,84],"multiple":[7],"sources":[8],"of":[9,47,111,131,141,215],"error":[10],"arising":[11],"from":[12,26,153],"imaging":[13,171],"conditions,":[14],"tracer":[15],"dynamics,":[16],"and":[17,50,71,92,137,160,167,183,204,224],"three-dimensional":[18],"flow":[19],"effects.":[20],"While":[21],"established":[22],"approaches":[23],"estimate":[24],"uncertainty":[25,76,189,198,234],"correlation":[27],"statistics":[28],"or":[29,105],"particle":[30],"matching,":[31],"convolutional":[32],"neural":[33],"network":[34,95],"(CNN)-based":[35],"PIV":[36,64,158,162],"methods":[37],"typically":[38],"provide":[39],"only":[40],"deterministic":[41,217],"displacement":[42,69,119],"fields,":[43],"limiting":[44],"quantitative":[45],"assessment":[46],"reconstruction":[48,209],"reliability":[49],"experimental":[51,157],"optimization.":[52],"In":[53],"this":[54],"work,":[55],"we":[56],"introduce":[57],"P-LIMA,":[58],"a":[59,72,97,128],"lean":[60],"probabilistic":[61,133],"CNN":[62],"for":[63],"that":[65,200],"jointly":[66],"reconstructs":[67],"the":[68,80,94,132,139,161,213,216],"field":[70],"spatially":[73],"resolved,":[74],"data-dependent":[75],"estimate.":[77],"P-LIMA":[78,196,232],"extends":[79],"lightweight":[81],"LIMA":[82],"architecture":[83],"augmenting":[85],"its":[86],"output":[87],"with":[88,208],"per-component":[89],"scale":[90],"parameters":[91,223],"training":[93,136],"using":[96],"heteroscedastic":[98],"negative":[99],"log-likelihood":[100],"formulation":[101],"based":[102],"on":[103,118,149,175,229],"Gaussian":[104],"Student-t":[106],"distributions,":[107],"enabling":[108],"robust":[109],"modeling":[110],"aleatoric":[112],"uncertainty.":[113,144],"Physically":[114],"motivated":[115],"regularization":[116],"terms":[117],"gradients":[120],"are":[121,201],"retained":[122],"to":[123,192,226],"ensure":[124],"spatial":[125],"consistency,":[126],"while":[127,211],"delayed":[129],"activation":[130],"loss":[134],"stabilizes":[135],"improves":[138],"calibration":[140],"hte":[142],"predicted":[143],"The":[145],"method":[146],"evaluated":[148],"synthetic":[150],"datasets":[151],"generated":[152],"direct":[154],"numerical":[155],"simulations,":[156],"measurements,":[159],"Challenge":[163],"3":[164],"(Case":[165],"B":[166],"C)":[168],"under":[169],"degraded":[170],"conditions.":[172],"Coverage":[173],"analysis":[174],"validation":[176],"data":[177],"shows":[178],"close":[179],"agreement":[180],"between":[181],"empirical":[182],"nominal":[184],"confidence":[185],"levels,":[186],"indicating":[187],"well-calibrated":[188],"estimates.":[190],"Compared":[191],"classical":[193],"correlation-based":[194],"approaches,":[195],"produces":[197],"fields":[199],"more":[202,205],"coherent":[203],"strongly":[206],"correlated":[207],"errors,":[210],"preserving":[212],"accuracy":[214],"baseline":[218],"model.":[219],"With":[220],"approximately":[221],"500k":[222],"real-time":[225],"near-real-time":[227],"inference":[228],"modern":[230],"GPUs,":[231],"provides":[233],"estimates":[235],"without":[236],"sacrificing":[237],"computational":[238],"efficiency.":[239]},"counts_by_year":[],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2026-09-02T00:00:00"}