{"id":"https://openalex.org/W7166731458","doi":"https://doi.org/10.7148/2026-0616","title":"A sensitivity-driven sampling reduction method for probabilistic approximations of odes","display_name":"A sensitivity-driven sampling reduction method for probabilistic approximations of odes","publication_year":2026,"publication_date":"2026-06-23","ids":{"openalex":"https://openalex.org/W7166731458","doi":"https://doi.org/10.7148/2026-0616"},"language":null,"primary_location":{"id":"doi:10.7148/2026-0616","is_oa":true,"landing_page_url":"https://doi.org/10.7148/2026-0616","pdf_url":"https://doi.org/10.7148/2026-0616","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ECMS 2026 Proceedings edited by Filippo Sanfilippo, Florenc Demrozi, Fabio Sgarbossa, Mohammad Poursina","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.7148/2026-0616","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5126845677","display_name":"Olivier Bou\u00ebt-Willaumez","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Olivier Bouet-Willaumez","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139650661","display_name":"Adrien Le Coent Le Coent","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Adrien Le Coent Le Coent","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014473489","display_name":"Beno\u00eet Barbot","orcid":"https://orcid.org/0000-0003-2417-3064"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Benoit Barbot","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5109464011","display_name":"Nihal Pekergin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nihal Pekergin","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":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"616","last_page":"623"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.791100025177002,"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/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.791100025177002,"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/T10848","display_name":"Advanced Multi-Objective Optimization Algorithms","score":0.060100000351667404,"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"}},{"id":"https://openalex.org/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.02329999953508377,"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/ode","display_name":"Ode","score":0.720300018787384},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5956000089645386},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.5734999775886536},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.5552999973297119},{"id":"https://openalex.org/keywords/reduction","display_name":"Reduction (mathematics)","score":0.5263000130653381},{"id":"https://openalex.org/keywords/ordinary-differential-equation","display_name":"Ordinary differential equation","score":0.48579999804496765},{"id":"https://openalex.org/keywords/importance-sampling","display_name":"Importance sampling","score":0.4648999869823456},{"id":"https://openalex.org/keywords/dynamical-systems-theory","display_name":"Dynamical systems theory","score":0.4230000078678131},{"id":"https://openalex.org/keywords/sensitivity","display_name":"Sensitivity (control systems)","score":0.41200000047683716}],"concepts":[{"id":"https://openalex.org/C34862557","wikidata":"https://www.wikidata.org/wiki/Q178985","display_name":"Ode","level":2,"score":0.720300018787384},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5956000089645386},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.5734999775886536},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.5552999973297119},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.5263000130653381},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.48820000886917114},{"id":"https://openalex.org/C51544822","wikidata":"https://www.wikidata.org/wiki/Q465274","display_name":"Ordinary differential equation","level":3,"score":0.48579999804496765},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.48190000653266907},{"id":"https://openalex.org/C52740198","wikidata":"https://www.wikidata.org/wiki/Q1539564","display_name":"Importance sampling","level":3,"score":0.4648999869823456},{"id":"https://openalex.org/C79379906","wikidata":"https://www.wikidata.org/wiki/Q3174497","display_name":"Dynamical systems theory","level":2,"score":0.4230000078678131},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.42149999737739563},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4205000102519989},{"id":"https://openalex.org/C21200559","wikidata":"https://www.wikidata.org/wiki/Q7451068","display_name":"Sensitivity (control systems)","level":2,"score":0.41200000047683716},{"id":"https://openalex.org/C2776459999","wikidata":"https://www.wikidata.org/wiki/Q2119376","display_name":"Fidelity","level":2,"score":0.40950000286102295},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.3944999873638153},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.3772999942302704},{"id":"https://openalex.org/C78045399","wikidata":"https://www.wikidata.org/wiki/Q11214","display_name":"Differential equation","level":2,"score":0.3368000090122223},{"id":"https://openalex.org/C2776544107","wikidata":"https://www.wikidata.org/wiki/Q5280367","display_name":"Direct methods","level":2,"score":0.3221000134944916},{"id":"https://openalex.org/C82142266","wikidata":"https://www.wikidata.org/wiki/Q3456604","display_name":"Dynamic Bayesian network","level":3,"score":0.3190000057220459},{"id":"https://openalex.org/C32230216","wikidata":"https://www.wikidata.org/wiki/Q7882499","display_name":"Uncertainty quantification","level":2,"score":0.3179999887943268},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.3131999969482422},{"id":"https://openalex.org/C93226319","wikidata":"https://www.wikidata.org/wiki/Q193137","display_name":"Differential (mechanical device)","level":2,"score":0.3003999888896942},{"id":"https://openalex.org/C77405623","wikidata":"https://www.wikidata.org/wiki/Q598451","display_name":"System dynamics","level":2,"score":0.29809999465942383},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.28299999237060547},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.2732999920845032},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.26980000734329224},{"id":"https://openalex.org/C94361409","wikidata":"https://www.wikidata.org/wiki/Q7882500","display_name":"Uncertainty reduction theory","level":2,"score":0.26159998774528503},{"id":"https://openalex.org/C33962884","wikidata":"https://www.wikidata.org/wiki/Q378637","display_name":"Dynamical system (definition)","level":3,"score":0.2615000009536743},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.2583000063896179}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.7148/2026-0616","is_oa":true,"landing_page_url":"https://doi.org/10.7148/2026-0616","pdf_url":"https://doi.org/10.7148/2026-0616","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ECMS 2026 Proceedings edited by Filippo Sanfilippo, Florenc Demrozi, Fabio Sgarbossa, Mohammad Poursina","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.7148/2026-0616","is_oa":true,"landing_page_url":"https://doi.org/10.7148/2026-0616","pdf_url":"https://doi.org/10.7148/2026-0616","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ECMS 2026 Proceedings edited by Filippo Sanfilippo, Florenc Demrozi, Fabio Sgarbossa, Mohammad Poursina","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7166731458.pdf","grobid_xml":"https://content.openalex.org/works/W7166731458.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"We":[0],"propose":[1],"a":[2,76,82,122],"sensitivity-driven":[3,101,144],"framework":[4],"for":[5],"constructing":[6],"Dynamic":[7],"Bayesian":[8],"Networks":[9],"(DBNs)":[10],"as":[11,81],"approximations":[12],"of":[13,24,68,125,135,150],"Ordinary":[14],"Differential":[15],"Equations":[16],"(ODEs)":[17],"models":[18,67],"while":[19,153],"reducing":[20],"the":[21,36,126,132,143,148,155,164],"computational":[22],"cost":[23],"generating":[25],"training":[26,78],"data.":[27],"The":[28,61],"approach":[29],"uses":[30],"global":[31],"sensitivity":[32],"rankings":[33],"to":[34,48,115,130,163],"identify":[35],"most":[37],"influential":[38],"direct":[39,97,105],"and":[40,84,99,106,129,158],"indirect":[41,108,137],"dynamical":[42,138],"dependencies,":[43],"which":[44],"are":[45],"then":[46],"used":[47],"define":[49],"reduced":[50,88],"sampling":[51,94,119],"supports":[52,102],"that":[53,142],"capture":[54],"essential":[55],"system":[56],"interactions":[57],"without":[58],"exhaustive":[59],"simulations.":[60],"methodology":[62],"is":[63,85],"evaluated":[64],"on":[65,91],"benchmark":[66],"progressively":[69],"higher":[70],"dimensionality.":[71],"A":[72],"DBN":[73],"built":[74],"from":[75],"full":[77,127],"dataset":[79],"serves":[80],"reference":[83],"compared":[86],"with":[87,161],"constructions":[89],"based":[90],"(i)":[92],"equation":[93],"using":[95],"only":[96],"dependencies":[98],"(ii)":[100],"incorporating":[103],"both":[104],"selected":[107],"influences.":[109],"This":[110],"experimental":[111],"setup":[112],"allows":[113],"us":[114],"assess":[116],"whether":[117],"equation-based":[118],"alone":[120],"provides":[121],"sufficient":[123],"approximation":[124],"model":[128],"quantify":[131],"additional":[133],"benefits":[134],"including":[136],"effects.":[139],"Results":[140],"show":[141],"strategy":[145],"drastically":[146],"reduces":[147],"number":[149],"required":[151],"simulations":[152],"maintaining":[154],"DBN\u2019s":[156],"structural":[157],"probabilistic":[159],"fidelity":[160],"respect":[162],"original":[165],"ODE":[166],"dynamics.":[167]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2026-07-01T00:00:00"}
