{"id":"https://openalex.org/W7162425432","doi":"https://doi.org/10.48550/arxiv.2605.24506","title":"Safe Data-Driven Control and Dynamical Learning via Constrained Neural Architectures and Koopman Operators","display_name":"Safe Data-Driven Control and Dynamical Learning via Constrained Neural Architectures and Koopman Operators","publication_year":2026,"publication_date":"2026-05-23","ids":{"openalex":"https://openalex.org/W7162425432","doi":"https://doi.org/10.48550/arxiv.2605.24506"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.24506","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.24506","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":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.24506","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5137053974","display_name":"Lin Feng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Feng, Lin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5137080221","display_name":"Xin He","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"He, Xin","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/T11206","display_name":"Model Reduction and Neural Networks","score":0.8044000267982483,"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":0.8044000267982483,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.09610000252723694,"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/T10805","display_name":"Vehicle Dynamics and Control Systems","score":0.013500000350177288,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/control-theory","display_name":"Control theory (sociology)","score":0.5102999806404114},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.47679999470710754},{"id":"https://openalex.org/keywords/nonlinear-system","display_name":"Nonlinear system","score":0.46320000290870667},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.45260000228881836},{"id":"https://openalex.org/keywords/parametric-statistics","display_name":"Parametric statistics","score":0.4092999994754791},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.40130001306533813},{"id":"https://openalex.org/keywords/linearization","display_name":"Linearization","score":0.3815000057220459},{"id":"https://openalex.org/keywords/stability","display_name":"Stability (learning theory)","score":0.36230000853538513},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.34929999709129333}],"concepts":[{"id":"https://openalex.org/C47446073","wikidata":"https://www.wikidata.org/wiki/Q5165890","display_name":"Control theory (sociology)","level":3,"score":0.5102999806404114},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.47679999470710754},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.46320000290870667},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.45339998602867126},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.45260000228881836},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.41130000352859497},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.4092999994754791},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.40130001306533813},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.38839998841285706},{"id":"https://openalex.org/C11210021","wikidata":"https://www.wikidata.org/wiki/Q1520713","display_name":"Linearization","level":3,"score":0.3815000057220459},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.36230000853538513},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.34929999709129333},{"id":"https://openalex.org/C157972887","wikidata":"https://www.wikidata.org/wiki/Q463359","display_name":"Convex optimization","level":3,"score":0.34549999237060547},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.34200000762939453},{"id":"https://openalex.org/C34862557","wikidata":"https://www.wikidata.org/wiki/Q178985","display_name":"Ode","level":2,"score":0.31790000200271606},{"id":"https://openalex.org/C183356978","wikidata":"https://www.wikidata.org/wiki/Q1779213","display_name":"Tracking error","level":3,"score":0.31610000133514404},{"id":"https://openalex.org/C51544822","wikidata":"https://www.wikidata.org/wiki/Q465274","display_name":"Ordinary differential equation","level":3,"score":0.31209999322891235},{"id":"https://openalex.org/C201829737","wikidata":"https://www.wikidata.org/wiki/Q2022760","display_name":"Linear matrix inequality","level":2,"score":0.310699999332428},{"id":"https://openalex.org/C51955184","wikidata":"https://www.wikidata.org/wiki/Q1545585","display_name":"Stochastic differential equation","level":2,"score":0.30820000171661377},{"id":"https://openalex.org/C157286648","wikidata":"https://www.wikidata.org/wiki/Q846780","display_name":"Kalman filter","level":2,"score":0.30550000071525574},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.2971999943256378},{"id":"https://openalex.org/C31531917","wikidata":"https://www.wikidata.org/wiki/Q915157","display_name":"Robust control","level":3,"score":0.2955999970436096},{"id":"https://openalex.org/C17020691","wikidata":"https://www.wikidata.org/wiki/Q139677","display_name":"Operator (biology)","level":5,"score":0.2937000095844269},{"id":"https://openalex.org/C91581856","wikidata":"https://www.wikidata.org/wiki/Q2707001","display_name":"Nonlinear control","level":3,"score":0.2856000065803528},{"id":"https://openalex.org/C112680207","wikidata":"https://www.wikidata.org/wiki/Q714886","display_name":"Regular polygon","level":2,"score":0.2736000120639801},{"id":"https://openalex.org/C167964875","wikidata":"https://www.wikidata.org/wiki/Q17011487","display_name":"Exponential stability","level":3,"score":0.27320000529289246},{"id":"https://openalex.org/C50897621","wikidata":"https://www.wikidata.org/wiki/Q2665508","display_name":"Hybrid system","level":2,"score":0.2687000036239624},{"id":"https://openalex.org/C79379906","wikidata":"https://www.wikidata.org/wiki/Q3174497","display_name":"Dynamical systems theory","level":2,"score":0.2574000060558319},{"id":"https://openalex.org/C172205157","wikidata":"https://www.wikidata.org/wiki/Q1782962","display_name":"Model predictive control","level":3,"score":0.2517000138759613}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.24506","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.24506","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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.24506","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.24506","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":"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":{"The":[0,139],"deployment":[1],"of":[2,43,120],"learning-based":[3],"models":[4,46,122],"in":[5,144,187,194],"safety-critical":[6],"control":[7],"systems":[8],"demands":[9],"mathematical":[10],"guarantees":[11],"that":[12,24],"standard":[13],"regression":[14],"architectures":[15],"cannot":[16],"provide.":[17],"This":[18],"paper":[19],"presents":[20],"an":[21,129,145],"integrated":[22],"framework":[23],"bridges":[25],"Neural":[26,64],"Ordinary":[27],"Differential":[28],"Equations":[29],"(Neural":[30],"ODEs),":[31],"measurement-induced":[32,100],"geometric":[33],"structures,":[34],"and":[35,60,166,176,190],"Koopman":[36],"operator":[37],"theory,":[38],"with":[39,202],"the":[40,87,95,117],"explicit":[41],"aim":[42],"producing":[44],"data-driven":[45],"whose":[47],"stability":[48,119],"certificates":[49],"are":[50,58],"computable,":[51],"not":[52],"merely":[53],"conjectured.":[54],"Three":[55],"complementary":[56],"components":[57],"developed":[59],"analyzed.":[61],"First,":[62],"ControlSynth":[63],"ODEs":[65],"enforce":[66],"global":[67],"convergence":[68],"through":[69],"tractable":[70],"linear":[71],"matrix":[72],"inequalities":[73],"(LMIs),":[74],"enabling":[75],"complex":[76],"nonlinear":[77,178],"dynamics":[78],"to":[79,106,158,183,199],"be":[80],"captured":[81],"without":[82],"sacrificing":[83],"boundedness":[84],"guarantees.":[85],"Second,":[86],"ICODE":[88],"formulation":[89],"incorporates":[90],"extrinsic":[91],"environmental":[92],"inputs":[93],"into":[94,134],"learned":[96],"vector":[97],"field,":[98],"while":[99],"bundle":[101],"structures":[102],"confine":[103],"state":[104,195],"trajectories":[105],"physically":[107],"admissible":[108],"manifolds.":[109],"Third,":[110],"a":[111,124,135,154,172,177,184,191],"systematic":[112],"ISS":[113],"verification":[114],"pipeline":[115],"certifies":[116],"input-to-state":[118],"Koopman-identified":[121],"via":[123],"convex":[125],"$L_2$-gain":[126],"LMI,":[127],"converting":[128],"otherwise":[130],"intractable":[131],"robustness":[132],"question":[133],"solvable":[136],"semidefinite":[137],"program.":[138],"certified":[140],"model":[141],"is":[142],"embedded":[143],"ICODE-MPPI":[146],"controller,":[147],"which":[148],"uses":[149],"continuous-time":[150],"residual":[151],"learning":[152],"inside":[153],"stochastic":[155],"sampling":[156],"loop":[157],"deliver":[159],"robust":[160],"path":[161],"tracking":[162,188],"under":[163],"parametric":[164],"uncertainty":[165],"persistent":[167],"disturbances.":[168],"Numerical":[169],"experiments":[170],"on":[171],"vehicle":[173],"path-tracking":[174],"benchmark":[175],"mechanical":[179],"oscillator":[180],"demonstrate":[181],"up":[182],"61\\%":[185],"reduction":[186,193],"RMSE":[189],"54\\%":[192],"estimation":[196],"error":[197],"relative":[198],"uncertified":[200],"baselines,":[201],"near-zero":[203],"LMI":[204],"violation":[205],"rates":[206],"across":[207],"all":[208],"evaluated":[209],"disturbance":[210],"levels.":[211]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-27T00:00:00"}