{"id":"https://openalex.org/W7138084014","doi":"https://doi.org/10.48550/arxiv.2603.15101","title":"Gaussian mixture models for model improvement","display_name":"Gaussian mixture models for model improvement","publication_year":2026,"publication_date":"2026-03-16","ids":{"openalex":"https://openalex.org/W7138084014","doi":"https://doi.org/10.48550/arxiv.2603.15101"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.15101","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.15101","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":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.15101","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5129696572","display_name":"Paolo Villani","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Villani, Paolo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035638107","display_name":"Daniel Andr\u00e9s Arcones","orcid":"https://orcid.org/0009-0003-2522-2179"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Arcones, Daniel Andr\u00e9s","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075972804","display_name":"J\u00f6rg F. Unger","orcid":"https://orcid.org/0000-0003-0035-0951"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Unger, J\u00f6rg F.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5129721681","display_name":"Martin Weiser","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Weiser, Martin","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/T10534","display_name":"Structural Health Monitoring Techniques","score":0.3027999997138977,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural 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/T10534","display_name":"Structural Health Monitoring Techniques","score":0.3027999997138977,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural 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/T11206","display_name":"Model Reduction and Neural Networks","score":0.29600000381469727,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.0860000029206276,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/calibration","display_name":"Calibration","score":0.692300021648407},{"id":"https://openalex.org/keywords/mixture-model","display_name":"Mixture model","score":0.557200014591217},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5343000292778015},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.4964999854564667},{"id":"https://openalex.org/keywords/fidelity","display_name":"Fidelity","score":0.4864000082015991},{"id":"https://openalex.org/keywords/physical-system","display_name":"Physical system","score":0.4327999949455261},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.4316999912261963},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.4189999997615814},{"id":"https://openalex.org/keywords/system-identification","display_name":"System identification","score":0.412200003862381}],"concepts":[{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.692300021648407},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.590499997138977},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.557200014591217},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5343000292778015},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.4964999854564667},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4909000098705292},{"id":"https://openalex.org/C2776459999","wikidata":"https://www.wikidata.org/wiki/Q2119376","display_name":"Fidelity","level":2,"score":0.4864000082015991},{"id":"https://openalex.org/C116672817","wikidata":"https://www.wikidata.org/wiki/Q1454986","display_name":"Physical system","level":2,"score":0.4327999949455261},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.4316999912261963},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.4189999997615814},{"id":"https://openalex.org/C119247159","wikidata":"https://www.wikidata.org/wiki/Q1366192","display_name":"System identification","level":3,"score":0.412200003862381},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.37400001287460327},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.3650999963283539},{"id":"https://openalex.org/C166550679","wikidata":"https://www.wikidata.org/wiki/Q263400","display_name":"Gaussian network model","level":3,"score":0.35899999737739563},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.35420000553131104},{"id":"https://openalex.org/C167928553","wikidata":"https://www.wikidata.org/wiki/Q1376021","display_name":"Estimation theory","level":2,"score":0.3343000113964081},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3149999976158142},{"id":"https://openalex.org/C127491075","wikidata":"https://www.wikidata.org/wiki/Q7617825","display_name":"Stochastic modelling","level":2,"score":0.2976999878883362},{"id":"https://openalex.org/C163175372","wikidata":"https://www.wikidata.org/wiki/Q3339222","display_name":"Linear model","level":2,"score":0.28130000829696655},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.28040000796318054},{"id":"https://openalex.org/C113364801","wikidata":"https://www.wikidata.org/wiki/Q26674","display_name":"High fidelity","level":2,"score":0.2759000062942505},{"id":"https://openalex.org/C115575686","wikidata":"https://www.wikidata.org/wiki/Q18822403","display_name":"Soft sensor","level":3,"score":0.271699994802475},{"id":"https://openalex.org/C2983447341","wikidata":"https://www.wikidata.org/wiki/Q1413083","display_name":"Model parameter","level":2,"score":0.2700999975204468},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2653999924659729},{"id":"https://openalex.org/C32230216","wikidata":"https://www.wikidata.org/wiki/Q7882499","display_name":"Uncertainty quantification","level":2,"score":0.262800008058548},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2572000026702881},{"id":"https://openalex.org/C76969082","wikidata":"https://www.wikidata.org/wiki/Q486902","display_name":"Mathematical model","level":2,"score":0.25450000166893005},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.25099998712539673}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.15101","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.15101","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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.15101","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.15101","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":"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":{"Modeling":[0],"complex":[1],"physical":[2,17,112],"systems":[3],"such":[4,92],"as":[5,93],"they":[6],"arise":[7],"in":[8,213],"civil":[9],"engineering":[10],"applications":[11],"requires":[12],"finding":[13],"a":[14,73,120,177,206,214],"trade-off":[15],"between":[16,53],"fidelity":[18],"and":[19,55,81,162,186,205],"practicality.":[20],"Consequently,":[21],"deviations":[22],"of":[23,48,76,131,144,183,210],"simulation":[24,54],"from":[25,41],"measurements":[26,56,80],"are":[27,57],"ubiquitous":[28],"even":[29],"after":[30],"model":[31,36,61,67,77,90,106,124,135,163],"calibration":[32,95],"due":[33,71],"to":[34,63,72,89,123,142,152],"the":[35,51,60,82,134,137,170,181,190],"discrepancy,":[37],"which":[38],"may":[39],"result":[40],"deliberate":[42],"modeling":[43],"decisions,":[44],"ignorance,":[45],"or":[46,104,114],"lack":[47,109],"knowledge.":[49],"If":[50],"mismatch":[52,98],"deemed":[58],"unacceptable,":[59],"has":[62],"be":[64],"improved.":[65],"Targeted":[66],"improvement":[68],"is":[69,174,196],"challenging":[70],"non-local":[74],"impact":[75],"discrepancies":[78,161],"on":[79,84],"dependence":[83],"sensor":[85,140,150],"configurations.":[86],"Many":[87],"approaches":[88],"improvement,":[91],"Bayesian":[94,178],"with":[96],"additive":[97],"terms,":[99],"gray-box":[100],"models,":[101],"symbolic":[102],"regression,":[103],"stochastic":[105],"updating,":[107],"often":[108],"interpretability,":[110],"generalizability,":[111],"consistency,":[113],"practical":[115],"applicability.":[116],"This":[117,156],"paper":[118],"introduces":[119],"non-intrusive":[121],"approach":[122,173],"discrepancy":[125],"analysis":[126],"using":[127],"mixture":[128],"models.":[129],"Instead":[130],"directly":[132],"modifying":[133],"structure,":[136],"method":[138],"maps":[139],"readings":[141,151],"clusters":[143,185],"physically":[145],"meaningful":[146],"parameters,":[147],"automatically":[148],"assigning":[149],"parameter":[153,184],"vector":[154],"clusters.":[155],"mapping":[157],"can":[158],"reveal":[159],"systematic":[160],"biases,":[164],"guiding":[165],"targeted,":[166],"physics-based":[167],"refinements":[168],"by":[169],"modeler.":[171],"The":[172,194],"formulated":[175],"within":[176],"framework,":[179],"enabling":[180],"identification":[182],"their":[187],"assignments":[188],"via":[189],"Expectation-Maximization":[191],"(EM)":[192],"algorithm.":[193],"methodology":[195],"demonstrated":[197],"through":[198],"numerical":[199],"experiments,":[200],"including":[201],"an":[202],"illustrative":[203],"example":[204],"real-world":[207],"case":[208],"study":[209],"heat":[211],"transfer":[212],"concrete":[215],"bridge.":[216]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-03-18T00:00:00"}