{"id":"https://openalex.org/W7140180648","doi":"https://doi.org/10.48550/arxiv.2603.20910","title":"LLM-ODE: Data-driven Discovery of Dynamical Systems with Large Language Models","display_name":"LLM-ODE: Data-driven Discovery of Dynamical Systems with Large Language Models","publication_year":2026,"publication_date":"2026-03-21","ids":{"openalex":"https://openalex.org/W7140180648","doi":"https://doi.org/10.48550/arxiv.2603.20910"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.20910","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.20910","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":null,"license_id":null,"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.2603.20910","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Bideh, Amirmohammad Ziaei","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bideh, Amirmohammad Ziaei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Gryak, Jonathan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gryak, Jonathan","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/T11975","display_name":"Evolutionary Algorithms and Applications","score":0.9699000120162964,"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/T11975","display_name":"Evolutionary Algorithms and Applications","score":0.9699000120162964,"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/T11948","display_name":"Machine Learning in Materials Science","score":0.007499999832361937,"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/T10848","display_name":"Advanced Multi-Objective Optimization Algorithms","score":0.003599999938160181,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.5781999826431274},{"id":"https://openalex.org/keywords/dynamical-systems-theory","display_name":"Dynamical systems theory","score":0.5764999985694885},{"id":"https://openalex.org/keywords/flexibility","display_name":"Flexibility (engineering)","score":0.5383999943733215},{"id":"https://openalex.org/keywords/scientific-discovery","display_name":"Scientific discovery","score":0.486299991607666},{"id":"https://openalex.org/keywords/genetic-programming","display_name":"Genetic programming","score":0.4489000141620636},{"id":"https://openalex.org/keywords/symbolic-regression","display_name":"Symbolic regression","score":0.4300000071525574},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.38420000672340393},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.35179999470710754}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7179999947547913},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.5781999826431274},{"id":"https://openalex.org/C79379906","wikidata":"https://www.wikidata.org/wiki/Q3174497","display_name":"Dynamical systems theory","level":2,"score":0.5764999985694885},{"id":"https://openalex.org/C2780598303","wikidata":"https://www.wikidata.org/wiki/Q65921492","display_name":"Flexibility (engineering)","level":2,"score":0.5383999943733215},{"id":"https://openalex.org/C2984917352","wikidata":"https://www.wikidata.org/wiki/Q12772819","display_name":"Scientific discovery","level":2,"score":0.486299991607666},{"id":"https://openalex.org/C110332635","wikidata":"https://www.wikidata.org/wiki/Q629498","display_name":"Genetic programming","level":2,"score":0.4489000141620636},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.44679999351501465},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4343999922275543},{"id":"https://openalex.org/C2776400721","wikidata":"https://www.wikidata.org/wiki/Q18171762","display_name":"Symbolic regression","level":3,"score":0.4300000071525574},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.40709999203681946},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.38420000672340393},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.35179999470710754},{"id":"https://openalex.org/C120567893","wikidata":"https://www.wikidata.org/wiki/Q1582085","display_name":"Knowledge extraction","level":2,"score":0.328900009393692},{"id":"https://openalex.org/C47822265","wikidata":"https://www.wikidata.org/wiki/Q854457","display_name":"Complex system","level":2,"score":0.32269999384880066},{"id":"https://openalex.org/C33962884","wikidata":"https://www.wikidata.org/wiki/Q378637","display_name":"Dynamical system (definition)","level":3,"score":0.3142000138759613},{"id":"https://openalex.org/C159149176","wikidata":"https://www.wikidata.org/wiki/Q14489129","display_name":"Evolutionary algorithm","level":2,"score":0.3075999915599823},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.3073999881744385},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.30090001225471497},{"id":"https://openalex.org/C114275822","wikidata":"https://www.wikidata.org/wiki/Q621512","display_name":"Linear dynamical system","level":3,"score":0.29440000653266907},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.2854999899864197},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.27070000767707825},{"id":"https://openalex.org/C50897621","wikidata":"https://www.wikidata.org/wiki/Q2665508","display_name":"Hybrid system","level":2,"score":0.2655999958515167},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.2567000091075897},{"id":"https://openalex.org/C34165917","wikidata":"https://www.wikidata.org/wiki/Q188267","display_name":"Programming paradigm","level":2,"score":0.2524999976158142},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2515999972820282}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.20910","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.20910","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.20910","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.20910","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Discovering":[0],"the":[1,59,97,112],"governing":[2],"equations":[3],"of":[4,58,100,115,135],"dynamical":[5,122],"systems":[6,123,162],"is":[7],"a":[8,26,77],"central":[9],"problem":[10],"across":[11],"many":[12],"scientific":[13,32],"disciplines.":[14],"As":[15],"experimental":[16],"data":[17],"become":[18],"increasingly":[19],"available,":[20],"automated":[21],"equation":[22],"discovery":[23,82,155,169],"methods":[24,132],"offer":[25],"promising":[27],"data-driven":[28],"approach":[29],"to":[30,45,64,160,164],"accelerate":[31],"discovery.":[33],"Among":[34],"these":[35,72],"methods,":[36],"genetic":[37],"programming":[38],"(GP)":[39],"has":[40],"been":[41],"widely":[42],"adopted":[43],"due":[44],"its":[46],"flexibility":[47],"and":[48,67,138,150,156,166],"interpretability.":[49],"However,":[50],"GP-based":[51,154],"approaches":[52],"often":[53],"suffer":[54],"from":[55,91],"inefficient":[56],"exploration":[57],"symbolic":[60,86],"search":[61,108,136],"space,":[62],"leading":[63],"slow":[65],"convergence":[66],"suboptimal":[68],"solutions.":[69],"To":[70],"address":[71],"limitations,":[73],"we":[74],"propose":[75],"LLM-ODE,":[76],"large":[78,101],"language":[79,102],"model-aided":[80],"model":[81,168],"framework":[83],"that":[84,125,145],"guides":[85],"evolution":[87],"using":[88],"patterns":[89],"extracted":[90],"elite":[92],"candidate":[93],"equations.":[94],"By":[95],"leveraging":[96],"generative":[98],"prior":[99],"models,":[103],"LLM-ODE":[104,126,146],"produces":[105],"more":[106],"informed":[107],"trajectories":[109],"while":[110],"preserving":[111],"exploratory":[113],"strengths":[114],"evolutionary":[116],"algorithms.":[117],"Empirical":[118],"results":[119,143],"on":[120],"91":[121],"show":[124],"variants":[127],"consistently":[128],"outperform":[129],"classical":[130],"GP":[131],"in":[133],"terms":[134],"efficiency":[137,149],"Pareto-front":[139],"quality.":[140],"Overall,":[141],"our":[142],"demonstrate":[144],"improves":[147],"both":[148],"accuracy":[151],"over":[152],"traditional":[153],"offers":[157],"greater":[158],"scalability":[159],"higher-dimensional":[161],"compared":[163],"linear":[165],"Transformer-only":[167],"methods.":[170]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-03-25T00:00:00"}
