{"id":"https://openalex.org/W7131833082","doi":"https://doi.org/10.48550/arxiv.2602.23089","title":"Physics-informed neural particle flow for the Bayesian update step","display_name":"Physics-informed neural particle flow for the Bayesian update step","publication_year":2026,"publication_date":"2026-02-26","ids":{"openalex":"https://openalex.org/W7131833082","doi":"https://doi.org/10.48550/arxiv.2602.23089"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2602.23089","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":null,"any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5037300214","display_name":"Domonkos Csuzdi","orcid":"https://orcid.org/0000-0003-4774-3330"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Csuzdi, Domonkos","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5092751865","display_name":"Tam\u00e1s B\u00e9csi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"B\u00e9csi, Tam\u00e1s","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5024105150","display_name":"Oliv\u00e9r T\u00f6r\u0151","orcid":"https://orcid.org/0000-0002-7288-5229"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"T\u00f6r\u0151, Oliv\u00e9r","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"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":0.8553000092506409,"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":0.8553000092506409,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.04800000041723251,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.026799999177455902,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5899999737739563},{"id":"https://openalex.org/keywords/nonlinear-system","display_name":"Nonlinear system","score":0.5825999975204468},{"id":"https://openalex.org/keywords/stochastic-differential-equation","display_name":"Stochastic differential equation","score":0.5063999891281128},{"id":"https://openalex.org/keywords/particle-filter","display_name":"Particle filter","score":0.4814000129699707},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.4415000081062317},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.4244000017642975},{"id":"https://openalex.org/keywords/partial-differential-equation","display_name":"Partial differential equation","score":0.4083000123500824},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.39719998836517334},{"id":"https://openalex.org/keywords/differential-equation","display_name":"Differential equation","score":0.39070001244544983}],"concepts":[{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5899999737739563},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.5825999975204468},{"id":"https://openalex.org/C51955184","wikidata":"https://www.wikidata.org/wiki/Q1545585","display_name":"Stochastic differential equation","level":2,"score":0.5063999891281128},{"id":"https://openalex.org/C52421305","wikidata":"https://www.wikidata.org/wiki/Q1151499","display_name":"Particle filter","level":3,"score":0.4814000129699707},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.453000009059906},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.44839999079704285},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.44679999351501465},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.4415000081062317},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.43959999084472656},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.4244000017642975},{"id":"https://openalex.org/C93779851","wikidata":"https://www.wikidata.org/wiki/Q271977","display_name":"Partial differential equation","level":2,"score":0.4083000123500824},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.39719998836517334},{"id":"https://openalex.org/C78045399","wikidata":"https://www.wikidata.org/wiki/Q11214","display_name":"Differential equation","level":2,"score":0.39070001244544983},{"id":"https://openalex.org/C197055811","wikidata":"https://www.wikidata.org/wiki/Q207522","display_name":"Probability density function","level":2,"score":0.36559998989105225},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3488999903202057},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.3467000126838684},{"id":"https://openalex.org/C32230216","wikidata":"https://www.wikidata.org/wiki/Q7882499","display_name":"Uncertainty quantification","level":2,"score":0.34610000252723694},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.34310001134872437},{"id":"https://openalex.org/C38349280","wikidata":"https://www.wikidata.org/wiki/Q1434290","display_name":"Flow (mathematics)","level":2,"score":0.3400999903678894},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.3314000070095062},{"id":"https://openalex.org/C2776036281","wikidata":"https://www.wikidata.org/wiki/Q48769818","display_name":"Constraint (computer-aided design)","level":2,"score":0.322299987077713},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.3206999897956848},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.304500013589859},{"id":"https://openalex.org/C91873725","wikidata":"https://www.wikidata.org/wiki/Q3445816","display_name":"Function approximation","level":3,"score":0.30379998683929443},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.2858000099658966},{"id":"https://openalex.org/C135252773","wikidata":"https://www.wikidata.org/wiki/Q1567213","display_name":"Inverse problem","level":2,"score":0.2773999869823456},{"id":"https://openalex.org/C57830394","wikidata":"https://www.wikidata.org/wiki/Q278079","display_name":"Posterior probability","level":3,"score":0.2653999924659729},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.26269999146461487}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2602.23089","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},{"id":"doi:10.48550/arxiv.2602.23089","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.23089","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":"article"}],"best_oa_location":{"id":"pmh:doi:10.48550/arxiv.2602.23089","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"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":{"The":[0],"Bayesian":[1],"update":[2,38],"step":[3],"poses":[4],"significant":[5],"computational":[6,171],"challenges":[7],"in":[8],"high-dimensional":[9],"nonlinear":[10,181],"estimation.":[11],"While":[12],"log-homotopy":[13,81],"particle":[14,66],"flow":[15],"filters":[16],"offer":[17],"an":[18,70,157],"alternative":[19],"to":[20,86,108,130,165,190],"stochastic":[21],"sampling,":[22],"existing":[23,31],"formulations":[24],"usually":[25],"yield":[26],"stiff":[27],"differential":[28,104],"equations.":[29],"Conversely,":[30],"deep":[32],"learning":[33],"approximations":[34],"typically":[35],"treat":[36],"the":[37,49,54,76,80,84,91,95,110,122,132,143,152,161],"as":[39,109,117,156],"a":[40,63,101,118,127,179],"black-box":[41],"task":[42],"or":[43],"rely":[44],"on":[45,175],"asymptotic":[46],"relaxation,":[47],"neglecting":[48],"exact":[50],"geometric":[51],"structure":[52],"of":[53,83],"finite-horizon":[55],"probability":[56],"transport.":[57],"In":[58],"this":[59,115],"work,":[60],"we":[61,78,125],"propose":[62],"physics-informed":[64],"neural":[65,128,153],"flow,":[67,77],"which":[68],"is":[69],"amortized":[71],"inference":[72],"framework.":[73],"To":[74],"construct":[75],"couple":[79],"trajectory":[82],"prior":[85],"posterior":[87,147],"density":[88,96],"function":[89],"with":[90],"continuity":[92],"equation":[93,105],"describing":[94],"evolution.":[97],"This":[98,136],"derivation":[99],"yields":[100],"governing":[102],"partial":[103],"(PDE),":[106],"referred":[107],"master":[111],"PDE.":[112],"By":[113],"embedding":[114],"PDE":[116],"physical":[119],"constraint":[120],"into":[121],"loss":[123],"function,":[124],"train":[126],"network":[129],"approximate":[131],"transport":[133],"velocity":[134],"field.":[135],"approach":[137],"enables":[138],"purely":[139],"unsupervised":[140],"training,":[141],"eliminating":[142],"need":[144],"for":[145],"ground-truth":[146],"samples.":[148],"We":[149],"demonstrate":[150],"that":[151],"parameterization":[154],"acts":[155],"implicit":[158],"regularizer,":[159],"mitigating":[160],"numerical":[162],"stiffness":[163],"inherent":[164],"analytic":[166],"flows":[167],"and":[168,178,187],"reducing":[169],"online":[170],"complexity.":[172],"Experimental":[173],"validation":[174],"multimodal":[176],"benchmarks":[177],"challenging":[180],"scenario":[182],"confirms":[183],"better":[184],"mode":[185],"coverage":[186],"robustness":[188],"compared":[189],"state-of-the-art":[191],"baselines.":[192]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-02-28T00:00:00"}
