{"id":"https://openalex.org/W7154504149","doi":"https://doi.org/10.48550/arxiv.2604.11929","title":"Fast and principled equation discovery from chaos to climate","display_name":"Fast and principled equation discovery from chaos to climate","publication_year":2026,"publication_date":"2026-04-13","ids":{"openalex":"https://openalex.org/W7154504149","doi":"https://doi.org/10.48550/arxiv.2604.11929"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.11929","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.11929","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"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":"https://doi.org/10.48550/arxiv.2604.11929","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5133702241","display_name":"Yuzheng Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Yuzheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133700353","display_name":"Weizhen Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Weizhen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5088184513","display_name":"Rui Carvalho","orcid":"https://orcid.org/0000-0002-3279-4218"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Carvalho, Rui","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":0.4611000120639801,"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":0.4611000120639801,"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/T11483","display_name":"Tropical and Extratropical Cyclones Research","score":0.057500001043081284,"subfield":{"id":"https://openalex.org/subfields/1902","display_name":"Atmospheric Science"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12611","display_name":"Neural Networks and Reservoir Computing","score":0.03200000151991844,"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/uncertainty-quantification","display_name":"Uncertainty quantification","score":0.6060000061988831},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.5047000050544739},{"id":"https://openalex.org/keywords/frequentist-inference","display_name":"Frequentist inference","score":0.5041000247001648},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4487000107765198},{"id":"https://openalex.org/keywords/chaotic","display_name":"Chaotic","score":0.41440001130104065},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.40290001034736633},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.3968000113964081},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.3691999912261963}],"concepts":[{"id":"https://openalex.org/C32230216","wikidata":"https://www.wikidata.org/wiki/Q7882499","display_name":"Uncertainty quantification","level":2,"score":0.6060000061988831},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5719000101089478},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.5047000050544739},{"id":"https://openalex.org/C162376815","wikidata":"https://www.wikidata.org/wiki/Q2158281","display_name":"Frequentist inference","level":4,"score":0.5041000247001648},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4487000107765198},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4250999987125397},{"id":"https://openalex.org/C2777052490","wikidata":"https://www.wikidata.org/wiki/Q5072826","display_name":"Chaotic","level":2,"score":0.41440001130104065},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.40290001034736633},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.3968000113964081},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.3691999912261963},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.36309999227523804},{"id":"https://openalex.org/C189285262","wikidata":"https://www.wikidata.org/wiki/Q1332350","display_name":"Multicollinearity","level":3,"score":0.3630000054836273},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.32409998774528503},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3208000063896179},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.32019999623298645},{"id":"https://openalex.org/C24552861","wikidata":"https://www.wikidata.org/wiki/Q2670177","display_name":"Data assimilation","level":2,"score":0.3188000023365021},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.2948000133037567},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2897999882698059},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.2867000102996826},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.2849999964237213},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.2651999890804291},{"id":"https://openalex.org/C47822265","wikidata":"https://www.wikidata.org/wiki/Q854457","display_name":"Complex system","level":2,"score":0.26179999113082886},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.25380000472068787},{"id":"https://openalex.org/C87007009","wikidata":"https://www.wikidata.org/wiki/Q210832","display_name":"Statistical hypothesis testing","level":2,"score":0.25029999017715454}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.11929","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.11929","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.11929","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.11929","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[{"display_name":"Climate action","id":"https://metadata.un.org/sdg/13","score":0.7772624492645264}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Our":[0],"ability":[1],"to":[2,136,204,221],"predict,":[3],"control,":[4],"and":[5,49,99,119,153,195,201],"ultimately":[6],"understand":[7],"complex":[8],"systems":[9,94,118,220],"rests":[10],"on":[11,91],"discovering":[12],"the":[13,84,126,177,222],"equations":[14,21,182],"that":[15,59,156],"govern":[16],"their":[17],"dynamics.":[18],"Identifying":[19],"these":[20,61],"directly":[22],"from":[23,199,217],"noisy,":[24],"limited":[25],"observations":[26,203],"has":[27],"therefore":[28],"become":[29],"a":[30,43,56,81,129,144,192,209],"central":[31],"challenge":[32],"in":[33,107,113,132],"data-driven":[34],"science,":[35],"yet":[36],"existing":[37,88],"library-based":[38],"sparse":[39],"regression":[40],"methods":[41,106],"force":[42],"compromise":[44],"between":[45],"automation,":[46],"statistical":[47,148],"rigor,":[48],"computational":[50,85,133],"efficiency.":[51],"Here":[52],"we":[53],"develop":[54],"Bayesian-ARGOS,":[55],"hybrid":[57],"framework":[58,211],"reconciles":[60],"demands":[62],"by":[63],"combining":[64],"rapid":[65],"frequentist":[66],"screening":[67],"with":[68,76,128,164,183],"focused":[69],"Bayesian":[70],"inference,":[71],"enabling":[72],"automated":[73],"equation":[74,213],"discovery":[75,214],"principled":[77],"uncertainty":[78],"quantification":[79],"at":[80],"fraction":[82],"of":[83,87,125,146,179],"cost":[86,134],"methods.":[89],"Tested":[90],"seven":[92],"chaotic":[93,219],"under":[95],"varying":[96],"data":[97,114],"scarcity":[98],"noise":[100,120],"levels,":[101],"Bayesian-ARGOS":[102,175,189],"outperforms":[103],"two":[104],"state-of-the-art":[105],"most":[108],"scenarios.":[109],"It":[110],"surpasses":[111],"SINDy":[112],"efficiency":[115],"for":[116,122,168,212],"all":[117],"tolerance":[121],"six":[123],"out":[124],"seven,":[127],"two-order-of-magnitude":[130],"reduction":[131],"compared":[135],"bootstrap-based":[137],"ARGOS.":[138],"The":[139],"probabilistic":[140],"formulation":[141],"additionally":[142],"enables":[143],"suite":[145],"standard":[147],"diagnostics,":[149],"including":[150],"influence":[151],"analysis":[152],"multicollinearity":[154],"detection":[155],"expose":[157],"failure":[158],"modes":[159],"otherwise":[160],"opaque.":[161],"When":[162],"integrated":[163],"representation":[165],"learning":[166],"(SINDy-SHRED)":[167],"high":[169],"dimensional":[170],"sea":[171],"surface":[172],"temperature":[173],"reconstruction,":[174],"increases":[176],"yield":[178],"valid":[180],"latent":[181,223],"significantly":[184],"improved":[185],"long":[186],"horizon":[187],"stability.":[188],"thus":[190],"provides":[191],"principled,":[193],"automated,":[194],"computationally":[196],"efficient":[197],"route":[198],"scarce":[200],"noisy":[202],"interpretable":[205],"governing":[206],"equations,":[207],"offering":[208],"practical":[210],"across":[215],"scales,":[216],"benchmark":[218],"dynamics":[224],"underlying":[225],"global":[226],"climate":[227],"patterns.":[228]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-04-16T00:00:00"}