{"id":"https://openalex.org/W7148321930","doi":"https://doi.org/10.48550/arxiv.2604.00256","title":"Informed Machine Learning with Knowledge Landmarks","display_name":"Informed Machine Learning with Knowledge Landmarks","publication_year":2026,"publication_date":"2026-03-31","ids":{"openalex":"https://openalex.org/W7148321930","doi":"https://doi.org/10.48550/arxiv.2604.00256"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.00256","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.00256","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":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.2604.00256","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5085508585","display_name":"Chuyi Dai","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Dai, Chuyi","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132801040","display_name":"Witold Pedrycz","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pedrycz, Witold","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132741768","display_name":"Suping Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Suping","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132809673","display_name":"Ding Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Ding","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5132782651","display_name":"Xianmin Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Xianmin","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5085508585"],"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":0.9294000267982483,"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":0.9294000267982483,"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/T11948","display_name":"Machine Learning in Materials Science","score":0.016100000590085983,"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/T12611","display_name":"Neural Networks and Reservoir Computing","score":0.009399999864399433,"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/generalization","display_name":"Generalization","score":0.6819999814033508},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5239999890327454},{"id":"https://openalex.org/keywords/granular-computing","display_name":"Granular computing","score":0.5056999921798706},{"id":"https://openalex.org/keywords/granularity","display_name":"Granularity","score":0.4860000014305115},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.44519999623298645},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.4300999939441681},{"id":"https://openalex.org/keywords/knowledge-extraction","display_name":"Knowledge extraction","score":0.4203000068664551},{"id":"https://openalex.org/keywords/domain-knowledge","display_name":"Domain knowledge","score":0.3986000120639801}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6944000124931335},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.6819999814033508},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6629999876022339},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6577000021934509},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5239999890327454},{"id":"https://openalex.org/C17209119","wikidata":"https://www.wikidata.org/wiki/Q5596712","display_name":"Granular computing","level":3,"score":0.5056999921798706},{"id":"https://openalex.org/C177774035","wikidata":"https://www.wikidata.org/wiki/Q1246948","display_name":"Granularity","level":2,"score":0.4860000014305115},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.44519999623298645},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.4300999939441681},{"id":"https://openalex.org/C120567893","wikidata":"https://www.wikidata.org/wiki/Q1582085","display_name":"Knowledge extraction","level":2,"score":0.4203000068664551},{"id":"https://openalex.org/C207685749","wikidata":"https://www.wikidata.org/wiki/Q2088941","display_name":"Domain knowledge","level":2,"score":0.3986000120639801},{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.39070001244544983},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.38089999556541443},{"id":"https://openalex.org/C24138899","wikidata":"https://www.wikidata.org/wiki/Q17141258","display_name":"Instance-based learning","level":3,"score":0.3580000102519989},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.3504999876022339},{"id":"https://openalex.org/C12426560","wikidata":"https://www.wikidata.org/wiki/Q189569","display_name":"Basis (linear algebra)","level":2,"score":0.3425999879837036},{"id":"https://openalex.org/C115925183","wikidata":"https://www.wikidata.org/wiki/Q1412694","display_name":"Knowledge-based systems","level":2,"score":0.30399999022483826},{"id":"https://openalex.org/C30542707","wikidata":"https://www.wikidata.org/wiki/Q1603203","display_name":"Commonsense knowledge","level":3,"score":0.30300000309944153},{"id":"https://openalex.org/C48164120","wikidata":"https://www.wikidata.org/wiki/Q4491893","display_name":"Concept learning","level":2,"score":0.2847999930381775},{"id":"https://openalex.org/C133462117","wikidata":"https://www.wikidata.org/wiki/Q4929239","display_name":"Data collection","level":2,"score":0.26589998602867126},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2639000117778778},{"id":"https://openalex.org/C13606891","wikidata":"https://www.wikidata.org/wiki/Q2623243","display_name":"Conceptual model","level":2,"score":0.2563000023365021}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.00256","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.00256","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":"doi:10.48550/arxiv.2604.00256","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.00256","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":false,"raw_source_name":null,"raw_type":"article"},"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":{"Informed":[0,43],"Machine":[1,10,44],"Learning":[2,11],"has":[3],"emerged":[4],"as":[5,57,119,124],"a":[6,15,25,49,109,125,142,181],"viable":[7],"generalization":[8],"of":[9,28,39,52,71,98,112,127,136,146,180,198,213,227,234,236],"(ML)":[12],"by":[13],"building":[14],"unified":[16,26],"conceptual":[17],"and":[18,30,79,91,106,122,150,210,215,217,231],"algorithmic":[19],"setting":[20],"for":[21,164],"constructing":[22],"models":[23],"on":[24,169,241],"basis":[27],"knowledge":[29,65,73,80,103,115,132,238],"data.":[31],"Physics-informed":[32],"ML":[33,254],"involving":[34],"physics":[35],"equations":[36],"is":[37,104],"one":[38],"the":[40,69,99,147,161,166,174,178,189,196,199,203,208,225,232,237,247],"developments":[41],"within":[42],"Learning.":[45],"This":[46],"study":[47],"proposes":[48],"novel":[50],"direction":[51],"Knowledge-Data":[53],"ML,":[54],"referred":[55],"to":[56,95,188,219],"KD-ML,":[58],"where":[59],"numeric":[60,171],"data":[61,78,87,214,228],"are":[62,81,88],"integrated":[63],"with":[64,224],"tidbits":[66],"expressed":[67],"in":[68,83,202],"form":[70],"granular":[72,182,190],"landmarks.":[74,133,239],"We":[75,194],"advocate":[76],"that":[77,185,246],"complementary":[82],"several":[84],"fundamental":[85],"ways:":[86],"precise":[89],"(numeric)":[90],"local,":[92],"usually":[93],"confined":[94],"some":[96,220],"region":[97],"input":[100],"space,":[101],"while":[102,173],"global":[105],"formulated":[107],"at":[108],"higher":[110],"level":[111,233],"abstraction.":[113],"The":[114],"can":[116],"be":[117],"represented":[118],"information":[120,129],"granules":[121,130],"organized":[123],"collection":[126],"input-output":[128],"called":[131],"In":[134],"virtue":[135],"this":[137],"evident":[138],"complementarity,":[139],"we":[140],"develop":[141],"comprehensive":[143],"design":[144],"process":[145],"KD-ML":[148],"model":[149,167,250],"formulate":[151],"an":[152],"original":[153],"augmented":[154],"loss":[155,204],"function":[156],"L,":[157],"which":[158,206],"additively":[159],"embraces":[160],"component":[162],"responsible":[163],"optimizing":[165],"based":[168],"available":[170],"data,":[172],"second":[175],"component,":[176],"playing":[177],"role":[179,197,212],"regularizer,":[183],"so":[184],"it":[186],"adheres":[187],"constraints":[191],"(knowledge":[192],"landmarks).":[193],"show":[195],"hyperparameter":[200],"positioned":[201],"function,":[205],"balances":[207],"contribution":[209],"guiding":[211],"knowledge,":[216],"point":[218],"essential":[221],"tendencies":[222],"associated":[223],"quality":[226],"(noise":[229],"level)":[230],"granularity":[235],"Experiments":[240],"two":[242],"physics-governed":[243],"benchmarks":[244],"demonstrate":[245],"proposed":[248],"KD":[249],"consistently":[251],"outperforms":[252],"data-driven":[253],"models.":[255]},"counts_by_year":[],"updated_date":"2026-04-03T16:44:17.987007","created_date":"2026-04-03T00:00:00"}
