{"id":"https://openalex.org/W7135023580","doi":"https://doi.org/10.1109/icsrs68021.2025.11422064","title":"Physics-Informed Neural Networks for Reliability Analysis of small and censored Lifetime Data","display_name":"Physics-Informed Neural Networks for Reliability Analysis of small and censored Lifetime Data","publication_year":2025,"publication_date":"2025-11-26","ids":{"openalex":"https://openalex.org/W7135023580","doi":"https://doi.org/10.1109/icsrs68021.2025.11422064"},"language":null,"primary_location":{"id":"doi:10.1109/icsrs68021.2025.11422064","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icsrs68021.2025.11422064","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 9th International Conference on System Reliability and Safety (ICSRS)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5030956698","display_name":"Niklas Nahrath","orcid":null},"institutions":[{"id":"https://openalex.org/I147869694","display_name":"Continental (Germany)","ror":"https://ror.org/0359s0245","country_code":"DE","type":"company","lineage":["https://openalex.org/I147869694"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Niklas Nahrath","raw_affiliation_strings":["Electronic Suspension Systems Continental AG,Hannover,Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Electronic Suspension Systems Continental AG,Hannover,Germany","institution_ids":["https://openalex.org/I147869694"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5030956698"],"corresponding_institution_ids":["https://openalex.org/I147869694"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.8465374,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"170","last_page":"177"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10968","display_name":"Statistical Distribution Estimation and Applications","score":0.5856999754905701,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10968","display_name":"Statistical Distribution Estimation and Applications","score":0.5856999754905701,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10928","display_name":"Probabilistic and Robust Engineering Design","score":0.2685999870300293,"subfield":{"id":"https://openalex.org/subfields/1804","display_name":"Statistics, Probability and Uncertainty"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10780","display_name":"Reliability and Maintenance Optimization","score":0.08980000019073486,"subfield":{"id":"https://openalex.org/subfields/2213","display_name":"Safety, Risk, Reliability and Quality"},"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/artificial-neural-network","display_name":"Artificial neural network","score":0.5698999762535095},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.5637999773025513},{"id":"https://openalex.org/keywords/parametric-statistics","display_name":"Parametric statistics","score":0.47749999165534973},{"id":"https://openalex.org/keywords/bootstrapping","display_name":"Bootstrapping (finance)","score":0.4677000045776367},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.46160000562667847},{"id":"https://openalex.org/keywords/weibull-distribution","display_name":"Weibull distribution","score":0.4596000015735626},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.39890000224113464},{"id":"https://openalex.org/keywords/markov-chain-monte-carlo","display_name":"Markov chain Monte Carlo","score":0.38769999146461487},{"id":"https://openalex.org/keywords/parametric-model","display_name":"Parametric model","score":0.3776000142097473}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5830000042915344},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5698999762535095},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.5637999773025513},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.47749999165534973},{"id":"https://openalex.org/C207609745","wikidata":"https://www.wikidata.org/wiki/Q4944086","display_name":"Bootstrapping (finance)","level":2,"score":0.4677000045776367},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.46160000562667847},{"id":"https://openalex.org/C173291955","wikidata":"https://www.wikidata.org/wiki/Q732332","display_name":"Weibull distribution","level":2,"score":0.4596000015735626},{"id":"https://openalex.org/C200601418","wikidata":"https://www.wikidata.org/wiki/Q2193887","display_name":"Reliability engineering","level":1,"score":0.436599999666214},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.40880000591278076},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.39890000224113464},{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.38769999146461487},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.38449999690055847},{"id":"https://openalex.org/C24574437","wikidata":"https://www.wikidata.org/wiki/Q7135228","display_name":"Parametric model","level":3,"score":0.3776000142097473},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.36880001425743103},{"id":"https://openalex.org/C2780598303","wikidata":"https://www.wikidata.org/wiki/Q65921492","display_name":"Flexibility (engineering)","level":2,"score":0.36250001192092896},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3598000109195709},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.3564000129699707},{"id":"https://openalex.org/C134261354","wikidata":"https://www.wikidata.org/wiki/Q938438","display_name":"Statistical inference","level":2,"score":0.3528999984264374},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.3352000117301941},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.31299999356269836},{"id":"https://openalex.org/C201729545","wikidata":"https://www.wikidata.org/wiki/Q2193887","display_name":"Reliability theory","level":3,"score":0.31200000643730164},{"id":"https://openalex.org/C16910744","wikidata":"https://www.wikidata.org/wiki/Q7705759","display_name":"Test data","level":2,"score":0.29339998960494995},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.2906999886035919},{"id":"https://openalex.org/C87007009","wikidata":"https://www.wikidata.org/wiki/Q210832","display_name":"Statistical hypothesis testing","level":2,"score":0.2815000116825104},{"id":"https://openalex.org/C2778045648","wikidata":"https://www.wikidata.org/wiki/Q176827","display_name":"Markov random field","level":4,"score":0.2799000144004822},{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.2775000035762787},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.26429998874664307}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icsrs68021.2025.11422064","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icsrs68021.2025.11422064","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 9th International Conference on System Reliability and Safety (ICSRS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":9,"referenced_works":["https://openalex.org/W2045602503","https://openalex.org/W2075470739","https://openalex.org/W3195312226","https://openalex.org/W4296177824","https://openalex.org/W4318215224","https://openalex.org/W4352981298","https://openalex.org/W4388476351","https://openalex.org/W4396941418","https://openalex.org/W4400325750"],"related_works":[],"abstract_inverted_index":{"This":[0],"study":[1],"investigates":[2],"the":[3,9,15,72,115,121,128,142,148,160,165,175,188,191,202,215,225],"practical":[4],"relevance":[5],"and":[6,20,30,53,75,123,164,198,201,220],"versatility":[7],"of":[8,17,117,141,174,187,227],"Physics-Informed":[10,167],"Neural":[11,168],"Network":[12,169],"methodology":[13],"in":[14,114,244],"context":[16],"reliability":[18,99,189,199,246],"demonstration":[19],"failure":[21,84,149,208],"forecasting.":[22],"Reliability":[23],"analysis":[24],"for":[25,50,195,206,241],"early":[26],"field":[27],"risk":[28],"assessments":[29],"lean":[31],"product":[32],"verification":[33],"is":[34,57,91,108,135,156],"particularly":[35],"challenging":[36],"when":[37],"only":[38],"a":[39,47,76,104,232,238],"few":[40],"failures":[41],"are":[42],"observed.":[43],"To":[44,97],"address":[45],"this,":[46],"semi-parametric":[48],"method":[49,82,183],"analyzing":[51],"small":[52],"censored":[54],"life":[55],"data":[56],"proposed":[58,166,216],"as":[59],"an":[60],"alternative":[61],"to":[62,93,137,158,211],"standard":[63,212],"maximum":[64],"likelihood":[65,233],"estimation.":[66],"The":[67,80,182],"approach":[68,217],"integrates":[69],"bootstrap":[70],"resampling,":[71],"Kaplan-Meier":[73],"estimator,":[74],"physics-informed":[77],"neural":[78,179],"network.":[79],"Kaplan=-Meier":[81],"estimates":[83,100,184],"probabilities,":[85],"while":[86,223],"fractional":[87],"random":[88],"weight":[89],"bootstrapping":[90],"used":[92,136],"construct":[94],"confidence":[95],"bounds.":[96],"extrapolate":[98],"beyond":[101],"observed":[102],"data,":[103],"machine":[105],"learning":[106],"algorithm":[107],"employed,":[109],"enhanced":[110],"with":[111,178],"prior":[112,139,172],"knowledge":[113,140,173],"form":[116],"physical":[118,176],"laws,":[119],"specifically,":[120],"Weibull":[122,143,162],"generalized":[124],"gamma":[125],"distributions,":[126],"making":[127],"final":[129],"step":[130],"semi-parametric.":[131],"Additionally,":[132],"Bayesian":[133],"inference":[134],"incorporate":[138],"shape":[144],"parameter,":[145],"which":[146],"reflects":[147],"mechanism.":[150],"Markov":[151],"Chain":[152],"Monte":[153],"Carlo":[154],"simulation":[155],"applied":[157],"both":[159,185],"parametric":[161,213],"model":[163],"method,":[170],"combining":[171],"law":[177],"network":[180],"parameters.":[181],"tails":[186],"curve:":[190],"lower":[192],"left":[193],"tail":[194,205],"test":[196],"planning":[197],"demonstration,":[200],"upper":[203],"right":[204],"future":[207],"prognosis.":[209],"Compared":[210],"methods,":[214],"delivers":[218],"robust":[219],"comparable":[221],"results,":[222],"offering":[224],"flexibility":[226],"non-parametric":[228],"methods":[229],"without":[230],"requiring":[231],"function.":[234],"It":[235],"also":[236],"provides":[237],"novel":[239],"framework":[240],"distribution":[242],"selection":[243],"conventional":[245],"analysis.":[247]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2026-03-13T00:00:00"}