{"id":"https://openalex.org/W7138920088","doi":"https://doi.org/10.48550/arxiv.2603.16980","title":"Interpretable AI-Assisted Early Reliability Prediction for a Two-Parameter Parallel Root-Finding Scheme","display_name":"Interpretable AI-Assisted Early Reliability Prediction for a Two-Parameter Parallel Root-Finding Scheme","publication_year":2026,"publication_date":"2026-03-17","ids":{"openalex":"https://openalex.org/W7138920088","doi":"https://doi.org/10.48550/arxiv.2603.16980"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.16980","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.16980","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.2603.16980","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5129991896","display_name":"Bruno Carpentieri","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Carpentieri, Bruno","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130186124","display_name":"Andrei Velichko","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Velichko, Andrei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088171412","display_name":"Mudassir Shams","orcid":"https://orcid.org/0000-0002-2980-5801"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shams, Mudassir","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5047664773","display_name":"Paola Lecca","orcid":"https://orcid.org/0000-0002-7224-136X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lecca, Paola","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"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":0.43470001220703125,"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":0.43470001220703125,"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/T10054","display_name":"Parallel Computing and Optimization Techniques","score":0.120899997651577,"subfield":{"id":"https://openalex.org/subfields/1708","display_name":"Hardware and Architecture"},"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/T10142","display_name":"Formal Methods in Verification","score":0.07079999893903732,"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/solver","display_name":"Solver","score":0.7074999809265137},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.6718000173568726},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.4334000051021576},{"id":"https://openalex.org/keywords/stability","display_name":"Stability (learning theory)","score":0.414900004863739},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.35920000076293945},{"id":"https://openalex.org/keywords/parameterized-complexity","display_name":"Parameterized complexity","score":0.35040000081062317},{"id":"https://openalex.org/keywords/proxy","display_name":"Proxy (statistics)","score":0.33480000495910645},{"id":"https://openalex.org/keywords/uncertainty-quantification","display_name":"Uncertainty quantification","score":0.3151000142097473}],"concepts":[{"id":"https://openalex.org/C2778770139","wikidata":"https://www.wikidata.org/wiki/Q1966904","display_name":"Solver","level":2,"score":0.7074999809265137},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.6718000173568726},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5202000141143799},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.4334000051021576},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4187000095844269},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.414900004863739},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.35920000076293945},{"id":"https://openalex.org/C165464430","wikidata":"https://www.wikidata.org/wiki/Q1570441","display_name":"Parameterized complexity","level":2,"score":0.35040000081062317},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.34619998931884766},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3382999897003174},{"id":"https://openalex.org/C2780148112","wikidata":"https://www.wikidata.org/wiki/Q1432581","display_name":"Proxy (statistics)","level":2,"score":0.33480000495910645},{"id":"https://openalex.org/C32230216","wikidata":"https://www.wikidata.org/wiki/Q7882499","display_name":"Uncertainty quantification","level":2,"score":0.3151000142097473},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.305400013923645},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.29789999127388},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.29789999127388},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2757999897003174},{"id":"https://openalex.org/C187191949","wikidata":"https://www.wikidata.org/wiki/Q1138496","display_name":"Profiling (computer programming)","level":2,"score":0.27489998936653137},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2685000002384186},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2669999897480011},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.2639000117778778},{"id":"https://openalex.org/C197640229","wikidata":"https://www.wikidata.org/wiki/Q2534066","display_name":"Predictability","level":2,"score":0.2623000144958496},{"id":"https://openalex.org/C176321772","wikidata":"https://www.wikidata.org/wiki/Q1430640","display_name":"Numerical stability","level":3,"score":0.26179999113082886},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.25999999046325684},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.2572000026702881},{"id":"https://openalex.org/C167928553","wikidata":"https://www.wikidata.org/wiki/Q1376021","display_name":"Estimation theory","level":2,"score":0.2565999925136566}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.16980","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.16980","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.2603.16980","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.16980","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":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"We":[0],"propose":[1],"an":[2],"interpretable":[3,173],"AI-assisted":[4],"reliability":[5,36,76,88],"diagnostic":[6],"framework":[7,99,171],"for":[8],"parameterized":[9],"root-finding":[10,113],"schemes":[11],"based":[12],"on":[13,109],"kNN-LLE":[14],"proxy":[15,62,95],"stability":[16,146,174],"profiling":[17],"and":[18,48,60,136,176],"multi-horizon":[19],"early":[20,44,91,178],"prediction.":[21],"The":[22,170],"approach":[23],"augments":[24],"a":[25,29,65,110,120],"numerical":[26],"solver":[27,35,103,181],"with":[28,156],"lightweight":[30],"predictive":[31],"layer":[32],"that":[33],"estimates":[34],"from":[37,73,90],"short":[38],"prefixes":[39],"of":[40,46,64,93,144],"iteration":[41],"dynamics,":[42],"enabling":[43],"identification":[45],"stable":[47],"unstable":[49],"parameter":[50,57],"regimes.":[51],"For":[52],"each":[53],"configuration":[54],"in":[55],"the":[56,87,94,98,123,140,145],"space,":[58],"raw":[59],"smoothed":[61],"profiles":[63],"largest":[66],"Lyapunov":[67],"exponent":[68],"(LLE)":[69],"estimator":[70],"are":[71,81],"constructed,":[72],"which":[74],"contractivity-based":[75],"scores":[77],"summarizing":[78],"finite-time":[79],"convergence":[80],"derived.":[82],"Machine":[83],"learning":[84],"models":[85,125],"predict":[86],"score":[89],"segments":[92],"profile,":[96],"allowing":[97],"to":[100,132,151],"determine":[101],"when":[102],"dynamics":[104],"become":[105],"diagnostically":[106],"informative.":[107],"Experiments":[108],"two-parameter":[111],"parallel":[112],"scheme":[114],"show":[115],"reliable":[116],"prediction":[117],"after":[118],"only":[119],"few":[121],"iterations:":[122],"best":[124],"achieve":[126],"R^2=0.48":[127],"at":[128,153],"horizon":[129],"T=1,":[130],"improve":[131],"R^2=0.67":[133],"by":[134],"T=3,":[135],"exceed":[137],"R^2=0.89":[138],"before":[139],"characteristic":[141],"minimum-location":[142],"scale":[143],"profile.":[147],"Prediction":[148],"accuracy":[149],"increases":[150],"R^2=0.96":[152],"larger":[154],"horizons,":[155],"mean":[157],"absolute":[158],"errors":[159],"around":[160],"0.03,":[161],"while":[162],"inference":[163],"costs":[164],"remain":[165],"negligible":[166],"(microseconds":[167],"per":[168],"sample).":[169],"provides":[172],"indicators":[175],"supports":[177],"decisions":[179],"during":[180],"execution,":[182],"such":[183],"as":[184],"continuing,":[185],"restarting,":[186],"or":[187],"adjusting":[188],"parameters.":[189]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-03-20T00:00:00"}
