{"id":"https://openalex.org/W7155383616","doi":"https://doi.org/10.48550/arxiv.2604.20707","title":"Generative Flow Networks for Model Adaptation in Digital Twins of Natural Systems","display_name":"Generative Flow Networks for Model Adaptation in Digital Twins of Natural Systems","publication_year":2026,"publication_date":"2026-04-22","ids":{"openalex":"https://openalex.org/W7155383616","doi":"https://doi.org/10.48550/arxiv.2604.20707"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.20707","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.20707","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":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.20707","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5060023448","display_name":"Pascal Archambault","orcid":"https://orcid.org/0009-0005-0185-9997"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Archambault, Pascal","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134435728","display_name":"Houari Sahraoui","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sahraoui, Houari","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5049129140","display_name":"Eugene Syriani","orcid":"https://orcid.org/0000-0001-6527-1651"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Syriani, Eugene","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/T10763","display_name":"Digital Transformation in Industry","score":0.18080000579357147,"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":0.18080000579357147,"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/T11206","display_name":"Model Reduction and Neural Networks","score":0.1404999941587448,"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/T10848","display_name":"Advanced Multi-Objective Optimization Algorithms","score":0.13609999418258667,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/adaptation","display_name":"Adaptation (eye)","score":0.7598999738693237},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6272000074386597},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.46630001068115234},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.4440999925136566},{"id":"https://openalex.org/keywords/natural","display_name":"Natural (archaeology)","score":0.42590001225471497},{"id":"https://openalex.org/keywords/calibration","display_name":"Calibration","score":0.4187000095844269}],"concepts":[{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.7598999738693237},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7042999863624573},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6272000074386597},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5153999924659729},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.46630001068115234},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.4440999925136566},{"id":"https://openalex.org/C2776608160","wikidata":"https://www.wikidata.org/wiki/Q4785462","display_name":"Natural (archaeology)","level":2,"score":0.42590001225471497},{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.4187000095844269},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4120999872684479},{"id":"https://openalex.org/C116672817","wikidata":"https://www.wikidata.org/wiki/Q1454986","display_name":"Physical system","level":2,"score":0.3149000108242035},{"id":"https://openalex.org/C2988105877","wikidata":"https://www.wikidata.org/wiki/Q1341685","display_name":"Inference system","level":5,"score":0.2962999939918518},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.287200003862381},{"id":"https://openalex.org/C134261354","wikidata":"https://www.wikidata.org/wiki/Q938438","display_name":"Statistical inference","level":2,"score":0.2825999855995178},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.26499998569488525},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.26260000467300415}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.20707","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.20707","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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.20707","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.20707","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":"Preprint"},"sustainable_development_goals":[{"display_name":"Zero hunger","score":0.7625024318695068,"id":"https://metadata.un.org/sdg/2"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Digital":[0],"twins":[1,79],"of":[2,80,138],"natural":[3,81],"systems":[4,10],"must":[5],"remain":[6],"aligned":[7],"with":[8,64,102],"physical":[9],"that":[11,96,131],"evolve":[12],"over":[13,91],"time,":[14],"are":[15,20],"only":[16],"partially":[17],"observed,":[18],"and":[19,47,56,113,146],"typically":[21],"modeled":[22],"by":[23],"mechanistic":[24,126],"simulators":[25],"whose":[26],"parameters":[27],"cannot":[28],"be":[29,100],"measured":[30],"directly.":[31],"In":[32],"such":[33],"settings,":[34],"model":[35,75],"adaptation":[36,76,85,140],"is":[37],"naturally":[38],"posed":[39],"as":[40,86],"a":[41,54,71,87,106,117,125],"simulation-based":[42],"inference":[43],"problem.":[44],"However,":[45],"sparse":[46],"indirect":[48],"observations":[49],"often":[50],"fail":[51],"to":[52,74,105],"identify":[53],"unique":[55],"optimal":[57],"calibration,":[58],"leaving":[59],"several":[60],"simulator":[61,93],"parameterizations":[62,98],"compatible":[63],"the":[65,132,139],"available":[66],"evidence.":[67],"This":[68],"article":[69],"presents":[70],"GFlowNet-based":[72],"approach":[73],"for":[77],"digital":[78],"systems.":[82],"We":[83],"formulate":[84],"generative":[88],"modeling":[89],"problem":[90],"complete":[92],"configurations,":[94],"so":[95],"plausible":[97,149],"can":[99],"sampled":[101],"probability":[103],"proportional":[104],"reward":[107],"derived":[108],"from":[109],"agreement":[110],"between":[111],"simulated":[112],"observed":[114],"behavior.":[115],"Using":[116],"controlled":[118],"environment":[119],"agriculture":[120],"case":[121],"study":[122],"based":[123],"on":[124],"tomato":[127],"model,":[128],"we":[129],"show":[130],"learned":[133],"policy":[134],"recovers":[135],"dominant":[136],"regions":[137],"landscape,":[141],"retrieves":[142],"strong":[143],"calibration":[144],"hypotheses,":[145],"preserves":[147],"multiple":[148],"configurations":[150],"under":[151],"uncertainty.":[152]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-24T00:00:00"}