{"id":"https://openalex.org/W4378474129","doi":"https://doi.org/10.48550/arxiv.2305.15174","title":"Simultaneous identification of models and parameters of scientific simulators","display_name":"Simultaneous identification of models and parameters of scientific simulators","publication_year":2023,"publication_date":"2023-05-24","ids":{"openalex":"https://openalex.org/W4378474129","doi":"https://doi.org/10.48550/arxiv.2305.15174"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2305.15174","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2305.15174","pdf_url":"https://arxiv.org/pdf/2305.15174","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-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2305.15174","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5010417177","display_name":"Cornelius Schr\u00f6der","orcid":"https://orcid.org/0000-0001-5643-2097"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Schr\u00f6der, Cornelius","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5080225154","display_name":"Jakob H. Macke","orcid":"https://orcid.org/0000-0001-5154-8912"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Macke, Jakob H.","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":true,"cited_by_count":3,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.983299970626831,"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"}},"topics":[{"id":"https://openalex.org/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.983299970626831,"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"}},{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9814000129699707,"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"}},{"id":"https://openalex.org/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.9765999913215637,"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/computer-science","display_name":"Computer science","score":0.7197032570838928},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5637966394424438},{"id":"https://openalex.org/keywords/model-selection","display_name":"Model selection","score":0.47389450669288635},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.4722817838191986},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.4431282877922058},{"id":"https://openalex.org/keywords/frequentist-inference","display_name":"Frequentist inference","score":0.43218204379081726},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.42889249324798584},{"id":"https://openalex.org/keywords/posterior-probability","display_name":"Posterior probability","score":0.42566072940826416},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4235001802444458},{"id":"https://openalex.org/keywords/joint-probability-distribution","display_name":"Joint probability distribution","score":0.416940838098526},{"id":"https://openalex.org/keywords/uncertainty-quantification","display_name":"Uncertainty quantification","score":0.4115099012851715},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.3936809301376343},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.35938698053359985},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.168081134557724}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7197032570838928},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5637966394424438},{"id":"https://openalex.org/C93959086","wikidata":"https://www.wikidata.org/wiki/Q6888345","display_name":"Model selection","level":2,"score":0.47389450669288635},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.4722817838191986},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.4431282877922058},{"id":"https://openalex.org/C162376815","wikidata":"https://www.wikidata.org/wiki/Q2158281","display_name":"Frequentist inference","level":4,"score":0.43218204379081726},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.42889249324798584},{"id":"https://openalex.org/C57830394","wikidata":"https://www.wikidata.org/wiki/Q278079","display_name":"Posterior probability","level":3,"score":0.42566072940826416},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4235001802444458},{"id":"https://openalex.org/C18653775","wikidata":"https://www.wikidata.org/wiki/Q1333358","display_name":"Joint probability distribution","level":2,"score":0.416940838098526},{"id":"https://openalex.org/C32230216","wikidata":"https://www.wikidata.org/wiki/Q7882499","display_name":"Uncertainty quantification","level":2,"score":0.4115099012851715},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.3936809301376343},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.35938698053359985},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.168081134557724},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2305.15174","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2305.15174","pdf_url":"https://arxiv.org/pdf/2305.15174","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-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"doi:10.48550/arxiv.2305.15174","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2305.15174","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":"pmh:oai:arXiv.org:2305.15174","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2305.15174","pdf_url":"https://arxiv.org/pdf/2305.15174","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-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3129832318","display_name":null,"funder_award_id":"2064/1 -390727645","funder_id":"https://openalex.org/F4320320879","funder_display_name":"Deutsche Forschungsgemeinschaft"},{"id":"https://openalex.org/G4262206430","display_name":null,"funder_award_id":"SFB 1233","funder_id":"https://openalex.org/F4320320879","funder_display_name":"Deutsche Forschungsgemeinschaft"},{"id":"https://openalex.org/G53513784","display_name":"SFB 1233: Robustheit des Sehens  Prinzipien der Inferenz und neuronale Mechanismen","funder_award_id":"276693517","funder_id":"https://openalex.org/F4320320879","funder_display_name":"Deutsche Forschungsgemeinschaft"},{"id":"https://openalex.org/G5415233182","display_name":null,"funder_award_id":"01IS18039A","funder_id":"https://openalex.org/F4320321114","funder_display_name":"Bundesministerium f\u00fcr Bildung und Forschung"},{"id":"https://openalex.org/G6755401401","display_name":null,"funder_award_id":"101089288","funder_id":"https://openalex.org/F4320320300","funder_display_name":"European Commission"},{"id":"https://openalex.org/G7907426250","display_name":"EXC 2064: Maschinelles Lernen: Neue Perspektiven f\u00fcr die Wissenschaft","funder_award_id":"390727645","funder_id":"https://openalex.org/F4320320879","funder_display_name":"Deutsche Forschungsgemeinschaft"},{"id":"https://openalex.org/G8915364236","display_name":null,"funder_award_id":"2064/1","funder_id":"https://openalex.org/F4320320879","funder_display_name":"Deutsche Forschungsgemeinschaft"}],"funders":[{"id":"https://openalex.org/F4320320300","display_name":"European Commission","ror":"https://ror.org/00k4n6c32"},{"id":"https://openalex.org/F4320320879","display_name":"Deutsche Forschungsgemeinschaft","ror":"https://ror.org/018mejw64"},{"id":"https://openalex.org/F4320321114","display_name":"Bundesministerium f\u00fcr Bildung und Forschung","ror":"https://ror.org/04pz7b180"}],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4378474129.pdf"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W88131178","https://openalex.org/W2086321337","https://openalex.org/W2078502317","https://openalex.org/W2962852620","https://openalex.org/W2786776292","https://openalex.org/W1998343736","https://openalex.org/W4210542671","https://openalex.org/W2461195884","https://openalex.org/W2010158090","https://openalex.org/W3045699787"],"abstract_inverted_index":{"Many":[0],"scientific":[1,129,159],"models":[2,130],"are":[3],"composed":[4],"of":[5,96],"multiple":[6,139],"discrete":[7],"components,":[8,30],"and":[9,38,78,126,133,143,150,170],"scientists":[10,164],"often":[11],"make":[12,171],"heuristic":[13],"decisions":[14],"about":[15],"which":[16,161],"components":[17,37,61,77,91,149,169],"to":[18,69,108,165],"include.":[19],"Bayesian":[20],"inference":[21,41,53,85],"provides":[22,153],"a":[23,51,93,121,154],"mathematical":[24],"framework":[25],"for":[26,157],"systematically":[27],"selecting":[28],"model":[29,36,57,64,76,84,90,125,141,148,168],"but":[31],"defining":[32],"prior":[33],"distributions":[34,73,88,99],"over":[35,59,74,89],"developing":[39],"associated":[40,79],"schemes":[42],"has":[43],"been":[44],"challenging.":[45],"We":[46,55,117],"approach":[47],"this":[48],"problem":[49],"in":[50,100],"simulation-based":[52,83],"framework:":[54],"define":[56],"priors":[58],"candidate":[60],"and,":[62],"from":[63,131],"simulations,":[65],"train":[66],"neural":[67],"networks":[68],"infer":[70],"joint":[71],"probability":[72],"both":[75],"parameters.":[80,151],"Our":[81],"method,":[82],"(SBMI),":[86],"represents":[87],"as":[92],"conditional":[94],"mixture":[95],"multivariate":[97],"binary":[98],"the":[101],"Grassmann":[102],"formalism.":[103],"SBMI":[104,119,152],"can":[105,137],"be":[106],"applied":[107],"any":[109],"compositional":[110],"stochastic":[111],"simulator":[112],"without":[113],"requiring":[114],"likelihood":[115],"evaluations.":[116],"evaluate":[118],"on":[120,127],"simple":[122],"time":[123],"series":[124],"two":[128],"neuroscience,":[132],"show":[134],"that":[135,144],"it":[136,145],"discover":[138],"data-consistent":[140],"configurations,":[142],"reveals":[146],"non-identifiable":[147],"powerful":[155],"tool":[156],"data-driven":[158],"inquiry":[160],"will":[162],"allow":[163],"identify":[166],"essential":[167],"uncertainty-informed":[172],"modelling":[173],"decisions.":[174]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":2}],"updated_date":"2026-07-13T07:31:44.756512","created_date":"2023-05-27T00:00:00"}