{"id":"https://openalex.org/W4417466084","doi":"https://doi.org/10.48550/arxiv.2512.13919","title":"Adaptive digital twins for predictive decision-making: Online Bayesian learning of transition dynamics","display_name":"Adaptive digital twins for predictive decision-making: Online Bayesian learning of transition dynamics","publication_year":2025,"publication_date":"2025-12-15","ids":{"openalex":"https://openalex.org/W4417466084","doi":"https://doi.org/10.48550/arxiv.2512.13919"},"language":null,"primary_location":{"id":"pmh:oai:arXiv.org:2512.13919","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2512.13919","pdf_url":"https://arxiv.org/pdf/2512.13919","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2512.13919","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5120845806","display_name":"Eugenio Varetti","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Varetti, Eugenio","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021009505","display_name":"Matteo Torzoni","orcid":"https://orcid.org/0000-0003-0027-9788"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Torzoni, Matteo","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061743114","display_name":"Marco Tezzele","orcid":"https://orcid.org/0000-0001-9747-6328"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tezzele, Marco","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5082126056","display_name":"Andrea Manzoni","orcid":"https://orcid.org/0000-0001-8277-2802"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Manzoni, Andrea","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5120845806"],"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/T10763","display_name":"Digital Transformation in Industry","score":0.5885000228881836,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing 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/T10763","display_name":"Digital Transformation in Industry","score":0.5885000228881836,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing 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/T11006","display_name":"BIM and Construction Integration","score":0.06360000371932983,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"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/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.05770000070333481,"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/markov-decision-process","display_name":"Markov decision process","score":0.6789000034332275},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.6317999958992004},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5697000026702881},{"id":"https://openalex.org/keywords/graphical-model","display_name":"Graphical model","score":0.5429999828338623},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.5102999806404114},{"id":"https://openalex.org/keywords/parametric-statistics","display_name":"Parametric statistics","score":0.43479999899864197},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.41130000352859497},{"id":"https://openalex.org/keywords/markov-process","display_name":"Markov process","score":0.40220001339912415},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.4009000062942505},{"id":"https://openalex.org/keywords/variable-order-bayesian-network","display_name":"Variable-order Bayesian network","score":0.3894999921321869}],"concepts":[{"id":"https://openalex.org/C106189395","wikidata":"https://www.wikidata.org/wiki/Q176789","display_name":"Markov decision process","level":3,"score":0.6789000034332275},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6413000226020813},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.6317999958992004},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5697000026702881},{"id":"https://openalex.org/C155846161","wikidata":"https://www.wikidata.org/wiki/Q1143367","display_name":"Graphical model","level":2,"score":0.5429999828338623},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5286999940872192},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5248000025749207},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.5102999806404114},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.43479999899864197},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.41130000352859497},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.40220001339912415},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.4009000062942505},{"id":"https://openalex.org/C71983512","wikidata":"https://www.wikidata.org/wiki/Q7915687","display_name":"Variable-order Bayesian network","level":4,"score":0.3894999921321869},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.3862999975681305},{"id":"https://openalex.org/C194232998","wikidata":"https://www.wikidata.org/wiki/Q1606712","display_name":"Transition (genetics)","level":3,"score":0.37860000133514404},{"id":"https://openalex.org/C2781089630","wikidata":"https://www.wikidata.org/wiki/Q21856745","display_name":"Realization (probability)","level":2,"score":0.37279999256134033},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.36660000681877136},{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.3400999903678894},{"id":"https://openalex.org/C2986087404","wikidata":"https://www.wikidata.org/wiki/Q15946010","display_name":"Online learning","level":2,"score":0.33469998836517334},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.3336000144481659},{"id":"https://openalex.org/C17098449","wikidata":"https://www.wikidata.org/wiki/Q176814","display_name":"Partially observable Markov decision process","level":4,"score":0.3215000033378601},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.3037000000476837},{"id":"https://openalex.org/C163836022","wikidata":"https://www.wikidata.org/wiki/Q6771326","display_name":"Markov model","level":3,"score":0.2994000017642975},{"id":"https://openalex.org/C52421305","wikidata":"https://www.wikidata.org/wiki/Q1151499","display_name":"Particle filter","level":3,"score":0.28630000352859497},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2822999954223633},{"id":"https://openalex.org/C188116033","wikidata":"https://www.wikidata.org/wiki/Q2664563","display_name":"Q-learning","level":3,"score":0.28029999136924744},{"id":"https://openalex.org/C101112237","wikidata":"https://www.wikidata.org/wiki/Q4874481","display_name":"Bayesian statistics","level":4,"score":0.2696000039577484},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.266400009393692},{"id":"https://openalex.org/C145912823","wikidata":"https://www.wikidata.org/wiki/Q113558","display_name":"Dynamics (music)","level":2,"score":0.2599000036716461},{"id":"https://openalex.org/C82142266","wikidata":"https://www.wikidata.org/wiki/Q3456604","display_name":"Dynamic Bayesian network","level":3,"score":0.2590999901294708},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.2587999999523163},{"id":"https://openalex.org/C122123141","wikidata":"https://www.wikidata.org/wiki/Q176623","display_name":"Random variable","level":2,"score":0.2572999894618988},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.2524999976158142},{"id":"https://openalex.org/C8272713","wikidata":"https://www.wikidata.org/wiki/Q176737","display_name":"Stochastic process","level":2,"score":0.25130000710487366},{"id":"https://openalex.org/C44492722","wikidata":"https://www.wikidata.org/wiki/Q327069","display_name":"Conditional probability","level":2,"score":0.25060001015663147}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2512.13919","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2512.13919","pdf_url":"https://arxiv.org/pdf/2512.13919","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2512.13919","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2512.13919","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":"public-domain","license_id":"https://openalex.org/licenses/public-domain","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2512.13919","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2512.13919","pdf_url":"https://arxiv.org/pdf/2512.13919","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"This":[0],"work":[1],"shows":[2],"how":[3],"adaptivity":[4],"can":[5],"enhance":[6],"value":[7],"realization":[8],"of":[9,63,86,139],"digital":[10,24,113],"twins":[11,25],"in":[12],"civil":[13],"engineering.":[14],"We":[15,75,124],"focus":[16],"on":[17,128],"adapting":[18],"the":[19,35,77,91],"state":[20,48,68],"transition":[21,49,64],"models":[22],"within":[23],"represented":[26],"through":[27,71,107],"probabilistic":[28],"graphical":[29],"models.":[30],"The":[31,110],"bi-directional":[32],"interaction":[33],"between":[34],"physical":[36],"and":[37,121,136],"virtual":[38],"domains":[39],"is":[40],"modeled":[41],"using":[42],"dynamic":[43,96],"Bayesian":[44,73],"networks.":[45],"By":[46],"treating":[47],"probabilities":[50],"as":[51],"random":[52],"variables":[53],"endowed":[54],"with":[55,88,98],"conjugate":[56],"priors,":[57],"we":[58,101],"enable":[59],"hierarchical":[60],"online":[61],"learning":[62],"dynamics":[65],"from":[66],"a":[67,83,129,140],"to":[69,80,90],"another":[70],"effortless":[72],"updates.":[74],"provide":[76],"mathematical":[78],"framework":[79,115],"account":[81],"for":[82],"larger":[84],"class":[85],"distributions":[87],"respect":[89],"current":[92],"literature.":[93],"To":[94],"compute":[95],"policies":[97],"precision":[99],"updates":[100],"solve":[102],"parametric":[103],"Markov":[104],"decision":[105],"processes":[106],"reinforcement":[108],"learning.":[109],"proposed":[111],"adaptive":[112],"twin":[114],"enjoys":[116],"enhanced":[117],"personalization,":[118],"increased":[119],"robustness,":[120],"improved":[122],"cost-effectiveness.":[123],"assess":[125],"our":[126],"approach":[127],"case":[130],"study":[131],"involving":[132],"structural":[133],"health":[134],"monitoring":[135],"maintenance":[137],"planning":[138],"railway":[141],"bridge.":[142]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-12-18T00:00:00"}