{"id":"https://openalex.org/W7133311965","doi":"https://doi.org/10.48550/arxiv.2603.01949","title":"Probabilistic Retrofitting of Learned Simulators","display_name":"Probabilistic Retrofitting of Learned Simulators","publication_year":2026,"publication_date":"2026-03-02","ids":{"openalex":"https://openalex.org/W7133311965","doi":"https://doi.org/10.48550/arxiv.2603.01949"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.01949","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.01949","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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.2603.01949","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5099282463","display_name":"Cristiana Diaconu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Diaconu, Cristiana","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078731429","display_name":"Miles Cranmer","orcid":"https://orcid.org/0000-0002-6458-3423"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cranmer, Miles","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5127934682","display_name":"Richard E. Turner","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Turner, Richard E.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5127973297","display_name":"Tanya Marwah","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Marwah, Tanya","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5097372488","display_name":"Payel Mukhopadhyay","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mukhopadhyay, Payel","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/T11206","display_name":"Model Reduction and Neural Networks","score":0.7332000136375427,"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"}},"topics":[{"id":"https://openalex.org/T11206","display_name":"Model Reduction and Neural Networks","score":0.7332000136375427,"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/T11948","display_name":"Machine Learning in Materials Science","score":0.0284000001847744,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10848","display_name":"Advanced Multi-Objective Optimization Algorithms","score":0.027400000020861626,"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/probabilistic-logic","display_name":"Probabilistic logic","score":0.8199999928474426},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.5486999750137329},{"id":"https://openalex.org/keywords/retrofitting","display_name":"Retrofitting","score":0.532800018787384},{"id":"https://openalex.org/keywords/adaptation","display_name":"Adaptation (eye)","score":0.3619999885559082},{"id":"https://openalex.org/keywords/scheme","display_name":"Scheme (mathematics)","score":0.3549000024795532},{"id":"https://openalex.org/keywords/statistical-model","display_name":"Statistical model","score":0.3495999872684479}],"concepts":[{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.8199999928474426},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6128000020980835},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.5486999750137329},{"id":"https://openalex.org/C2778368411","wikidata":"https://www.wikidata.org/wiki/Q24662","display_name":"Retrofitting","level":2,"score":0.532800018787384},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.40389999747276306},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.3619999885559082},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.3549000024795532},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.3495999872684479},{"id":"https://openalex.org/C2777052490","wikidata":"https://www.wikidata.org/wiki/Q5072826","display_name":"Chaotic","level":2,"score":0.34549999237060547},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.33379998803138733},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.31299999356269836},{"id":"https://openalex.org/C115901376","wikidata":"https://www.wikidata.org/wiki/Q184199","display_name":"Automation","level":2,"score":0.28619998693466187},{"id":"https://openalex.org/C143017306","wikidata":"https://www.wikidata.org/wiki/Q3318133","display_name":"Probabilistic relevance model","level":4,"score":0.28110000491142273},{"id":"https://openalex.org/C116672817","wikidata":"https://www.wikidata.org/wiki/Q1454986","display_name":"Physical system","level":2,"score":0.26420000195503235},{"id":"https://openalex.org/C52063229","wikidata":"https://www.wikidata.org/wiki/Q7246845","display_name":"Probabilistic CTL","level":4,"score":0.2554999887943268}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.01949","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.01949","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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.2603.01949","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.01949","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":{"Dominant":[0],"approaches":[1],"for":[2,106],"modelling":[3,192],"Partial":[4],"Differential":[5],"Equations":[6],"(PDEs)":[7],"rely":[8],"on":[9,109,139,145,150],"deterministic":[10,46,59,132,178,205],"predictions,":[11],"yet":[12],"many":[13],"physical":[14],"systems":[15,147],"of":[16,104,153,164],"interest":[17],"are":[18],"inherently":[19],"chaotic":[20],"and":[21,35,120,148,170,184],"uncertain.":[22],"While":[23],"training":[24,210],"probabilistic":[25,62,159,190],"models":[26,60,107],"from":[27,197,203],"scratch":[28],"is":[29,32,80],"possible,":[30],"it":[31,82],"computationally":[33],"expensive":[34],"fails":[36],"to":[37,56,122,130,155,166,172,177],"leverage":[38],"the":[39,71,84,151],"significant":[40],"resources":[41],"already":[42],"invested":[43],"in":[44,117,125,168,174],"high-performing":[45],"backbones.":[47],"In":[48],"this":[49,78],"work,":[50],"we":[51,113],"adopt":[52],"a":[53,67,140],"training-efficient":[54],"strategy":[55],"transform":[57],"pre-trained":[58],"into":[61],"ones":[63],"via":[64],"retrofitting":[65],"with":[66,92,207],"proper":[68],"scoring":[69],"rule:":[70],"Continuous":[72],"Ranked":[73],"Probability":[74],"Score":[75],"(CRPS).":[76],"Crucially,":[77],"approach":[79,138],"architecture-agnostic:":[81],"applies":[83],"same":[85],"adaptation":[86,160],"mechanism":[87],"across":[88,101,181],"distinct":[89],"model":[90],"backbones":[91,206],"minimal":[93],"code":[94],"modifications.":[95],"The":[96],"method":[97],"proves":[98],"highly":[99],"effective":[100],"different":[102],"scales":[103],"pre-training:":[105],"trained":[108,144],"single":[110],"dynamical":[111],"systems,":[112],"achieve":[114],"20-54%":[115],"reductions":[116],"rollout":[118],"CRPS":[119,169],"up":[121,165,171],"30%":[123],"improvements":[124],"variance-normalised":[126],"RMSE":[127],"(VRMSE)":[128],"relative":[129],"compute-matched":[131],"fine-tuning.":[133,179],"We":[134],"further":[135],"validate":[136],"our":[137,158,186],"PDE":[141,191],"foundation":[142],"model,":[143],"multiple":[146],"retrofitted":[149],"dataset":[152],"interest,":[154],"show":[156,188],"that":[157,189],"yields":[161],"an":[162],"improvement":[163],"40%":[167],"15%":[173],"VRMSE":[175],"compared":[176],"Validated":[180],"diverse":[182],"architectures":[183],"dynamics,":[185],"results":[187],"need":[193],"not":[194],"require":[195],"retraining":[196],"scratch,":[198],"but":[199],"can":[200],"be":[201],"unlocked":[202],"existing":[204],"modest":[208],"additional":[209],"cost.":[211]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-03-04T00:00:00"}
