{"id":"https://openalex.org/W7148500996","doi":"https://doi.org/10.48550/arxiv.2604.01169","title":"Bridging the Simulation-to-Experiment Gap with Generative Models using Adversarial Distribution Alignment","display_name":"Bridging the Simulation-to-Experiment Gap with Generative Models using Adversarial Distribution Alignment","publication_year":2026,"publication_date":"2026-04-01","ids":{"openalex":"https://openalex.org/W7148500996","doi":"https://doi.org/10.48550/arxiv.2604.01169"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.01169","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.01169","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.2604.01169","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5069060993","display_name":"Kai Nelson","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nelson, Kai","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5092977673","display_name":"Tobias Kreiman","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kreiman, Tobias","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132801348","display_name":"Sergey Levine","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Levine, Sergey","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5049020441","display_name":"Aditi S. Krishnapriyan","orcid":"https://orcid.org/0000-0003-3472-6080"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Krishnapriyan, Aditi S.","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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.41179999709129333,"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"}},"topics":[{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.41179999709129333,"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"}},{"id":"https://openalex.org/T11948","display_name":"Machine Learning in Materials Science","score":0.3580000102519989,"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/T11206","display_name":"Model Reduction and Neural Networks","score":0.03480000048875809,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/bridging","display_name":"Bridging (networking)","score":0.7876999974250793},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.7328000068664551},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.6434999704360962},{"id":"https://openalex.org/keywords/observable","display_name":"Observable","score":0.524399995803833},{"id":"https://openalex.org/keywords/physical-system","display_name":"Physical system","score":0.5141000151634216},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.4278999865055084},{"id":"https://openalex.org/keywords/distribution","display_name":"Distribution (mathematics)","score":0.3767000138759613}],"concepts":[{"id":"https://openalex.org/C174348530","wikidata":"https://www.wikidata.org/wiki/Q188635","display_name":"Bridging (networking)","level":2,"score":0.7876999974250793},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.7328000068664551},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6930999755859375},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.6434999704360962},{"id":"https://openalex.org/C32848918","wikidata":"https://www.wikidata.org/wiki/Q845789","display_name":"Observable","level":2,"score":0.524399995803833},{"id":"https://openalex.org/C116672817","wikidata":"https://www.wikidata.org/wiki/Q1454986","display_name":"Physical system","level":2,"score":0.5141000151634216},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4392000138759613},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.43689998984336853},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.4278999865055084},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.4163999855518341},{"id":"https://openalex.org/C110121322","wikidata":"https://www.wikidata.org/wiki/Q865811","display_name":"Distribution (mathematics)","level":2,"score":0.3767000138759613},{"id":"https://openalex.org/C55037315","wikidata":"https://www.wikidata.org/wiki/Q5421151","display_name":"Experimental data","level":2,"score":0.37389999628067017},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.34200000762939453},{"id":"https://openalex.org/C73301696","wikidata":"https://www.wikidata.org/wiki/Q5469984","display_name":"Formalism (music)","level":3,"score":0.31540000438690186},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2888999879360199},{"id":"https://openalex.org/C47822265","wikidata":"https://www.wikidata.org/wiki/Q854457","display_name":"Complex system","level":2,"score":0.2881999909877777},{"id":"https://openalex.org/C32230216","wikidata":"https://www.wikidata.org/wiki/Q7882499","display_name":"Uncertainty quantification","level":2,"score":0.2533999979496002},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2524999976158142},{"id":"https://openalex.org/C194583477","wikidata":"https://www.wikidata.org/wiki/Q408891","display_name":"Physical law","level":2,"score":0.25130000710487366}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.01169","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.01169","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.2604.01169","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.01169","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":{"A":[0],"fundamental":[1],"challenge":[2],"in":[3,113],"science":[4],"and":[5,172],"engineering":[6],"is":[7,107,188],"the":[8,50,64,114,142,153],"simulation-to-experiment":[9,79],"gap.":[10],"While":[11,104],"we":[12,109],"often":[13],"possess":[14],"prior":[15],"knowledge":[16],"of":[17,58,101,129,144],"physical":[18,21,115],"laws,":[19],"these":[20],"laws":[22],"can":[23,179],"be":[24],"too":[25],"difficult":[26],"to":[27],"solve":[28],"exactly":[29],"for":[30],"complex":[31],"systems.":[32],"Such":[33],"systems":[34],"are":[35],"commonly":[36],"modeled":[37],"using":[38],"simulators,":[39],"which":[40],"impose":[41],"computational":[42],"approximations.":[43],"Meanwhile,":[44],"experimental":[45,54,102,145,173],"measurements":[46],"more":[47],"faithfully":[48],"represent":[49],"real":[51],"world,":[52],"but":[53],"data":[55],"typically":[56],"consists":[57],"observations":[59,100],"that":[60,76,149,177],"only":[61],"partially":[62],"reflect":[63],"system's":[65],"full":[66],"underlying":[67],"state.":[68],"We":[69,147,163],"propose":[70],"a":[71,83,126,136],"data-driven":[72],"distribution":[73,139,143],"alignment":[74],"framework":[75,168],"bridges":[77],"this":[78],"gap":[80],"by":[81,117],"pre-training":[82],"generative":[84,127,181],"model":[85,128],"on":[86,135,169],"fully":[87],"observed":[88],"(but":[89,98],"imperfect)":[90],"simulation":[91],"data,":[92,175],"then":[93],"aligning":[94],"it":[95,178],"with":[96,141,158,183],"partial":[97],"real)":[99],"data.":[103],"our":[105,111,150,167],"method":[106,124,151],"domain-agnostic,":[108],"ground":[110],"approach":[112],"sciences":[116],"introducing":[118],"Adversarial":[119],"Distribution":[120],"Alignment":[121],"(ADA).":[122],"This":[123],"aligns":[125],"atomic":[130],"positions":[131],"--":[132,140],"initially":[133],"trained":[134],"simulated":[137],"Boltzmann":[138],"observations.":[146],"prove":[148],"recovers":[152],"target":[154],"observable":[155],"distribution,":[156],"even":[157],"multiple,":[159],"potentially":[160],"correlated":[161],"observables.":[162,185],"also":[164],"empirically":[165],"validate":[166],"synthetic,":[170],"molecular,":[171],"protein":[174],"demonstrating":[176],"align":[180],"models":[182],"diverse":[184],"Our":[186],"code":[187],"available":[189],"at":[190],"https://kaityrusnelson.com/ada/.":[191]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-04-03T00:00:00"}
