{"id":"https://openalex.org/W7203804284","doi":null,"title":"Development of Probabilistic Surrogate Model for Uncertainty Quantification in Low Velocity Impact (LVI) of Caron/Epoxy Woven Composites","display_name":"Development of Probabilistic Surrogate Model for Uncertainty Quantification in Low Velocity Impact (LVI) of Caron/Epoxy Woven Composites","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7203804284"},"language":"en","primary_location":{"id":"pmh:oai:elib.dlr.de:226108","doi":null,"is_oa":false,"landing_page_url":"https://elib.dlr.de/226108/","pdf_url":null,"source":{"id":"https://openalex.org/S4377196266","display_name":"elib (German Aerospace Center)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I2898391981","host_organization_name":"Deutsches Zentrum f\u00fcr Luft- und Raumfahrt e. V. (DLR)","host_organization_lineage":["https://openalex.org/I2898391981"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Konferenzbeitrag"},"volume":null,"type":"conference-paper","indexed_in":[],"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/A5003606405","display_name":"Sanghyun Yoo","orcid":"https://orcid.org/0000-0001-6924-1716","observed_orcids":["https://orcid.org/0000-0001-6924-1716"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yoo, Sanghyun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055783874","display_name":"Mathieu Vinot","orcid":"https://orcid.org/0000-0003-3394-5142","observed_orcids":["https://orcid.org/0000-0003-3394-5142"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Vinot, Mathieu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138364740","display_name":"Nathalie Toso","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Toso, Nathalie","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5085678072","display_name":"Heinz F. Voggenreiter","orcid":"https://orcid.org/0009-0008-1940-8248","observed_orcids":["https://orcid.org/0009-0008-1940-8248"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Voggenreiter, Heinz","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":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.66651736,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"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/T10219","display_name":"Mechanical Behavior of Composites","score":0.8544999957084656,"subfield":{"id":"https://openalex.org/subfields/2211","display_name":"Mechanics of Materials"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10219","display_name":"Mechanical Behavior of Composites","score":0.8544999957084656,"subfield":{"id":"https://openalex.org/subfields/2211","display_name":"Mechanics of Materials"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10928","display_name":"Probabilistic and Robust Engineering Design","score":0.10369999706745148,"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/T11948","display_name":"Machine Learning in Materials Science","score":0.007899999618530273,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/low-velocity-impact","display_name":"low-velocity impact","score":0.8299999833106995},{"id":"https://openalex.org/keywords/probabilistic-surrogate-models","display_name":"probabilistic surrogate models","score":0.9100000262260437},{"id":"https://openalex.org/keywords/uncertainty-quantification","display_name":"uncertainty quantification","score":0.9390000104904175},{"id":"https://openalex.org/keywords/carbon-fiber-reinforced-polymer","display_name":"carbon fiber reinforced polymer","score":0.6880000233650208},{"id":"https://openalex.org/keywords/bayesian-neural-networks","display_name":"Bayesian neural networks","score":0.8730000257492065},{"id":"https://openalex.org/keywords/monte-carlo-dropout","display_name":"Monte Carlo dropout","score":0.9599999785423279},{"id":"https://openalex.org/keywords/gaussian-process-regression","display_name":"Gaussian process regression","score":0.9980000257492065},{"id":"https://openalex.org/keywords/building-block-approach","display_name":"building block approach","score":0.6759999990463257},{"id":"https://openalex.org/keywords/barely-visible-impact-damage","display_name":"barely visible impact damage","score":0.765999972820282},{"id":"https://openalex.org/keywords/energy-absorption","display_name":"energy absorption","score":0.8999999761581421},{"id":"https://openalex.org/keywords/impact-damage-prediction","display_name":"impact damage prediction","score":0.46000000834465027},{"id":"https://openalex.org/keywords/latin-hypercube-sampling","display_name":"Latin hypercube sampling","score":0.6710000038146973},{"id":"https://openalex.org/keywords/shap","display_name":"SHAP","score":0.8259999752044678},{"id":"https://openalex.org/keywords/machine-learning-surrogate-models","display_name":"machine learning surrogate models","score":0.5690000057220459},{"id":"https://openalex.org/keywords/finite-element-analysis","display_name":"finite element analysis","score":0.44999998807907104}],"concepts":[{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6542999744415283},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.6467000246047974},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5475999712944031},{"id":"https://openalex.org/C131675550","wikidata":"https://www.wikidata.org/wiki/Q7646884","display_name":"Surrogate model","level":2,"score":0.5467000007629395},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5400999784469604},{"id":"https://openalex.org/C32230216","wikidata":"https://www.wikidata.org/wiki/Q7882499","display_name":"Uncertainty quantification","level":2,"score":0.5396000146865845},{"id":"https://openalex.org/C200601418","wikidata":"https://www.wikidata.org/wiki/Q2193887","display_name":"Reliability engineering","level":1,"score":0.5318999886512756},{"id":"https://openalex.org/C2779307704","wikidata":"https://www.wikidata.org/wiki/Q11794832","display_name":"Coupon","level":2,"score":0.49390000104904175},{"id":"https://openalex.org/C46304622","wikidata":"https://www.wikidata.org/wiki/Q374814","display_name":"Certification","level":2,"score":0.4722000062465668},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.4228000044822693},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.4113999903202057},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.4043999910354614},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.3977000117301941},{"id":"https://openalex.org/C167740415","wikidata":"https://www.wikidata.org/wiki/Q2876213","display_name":"Aerospace","level":2,"score":0.39739999175071716},{"id":"https://openalex.org/C2780615140","wikidata":"https://www.wikidata.org/wiki/Q920419","display_name":"Upgrade","level":2,"score":0.3905999958515167},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.34940001368522644},{"id":"https://openalex.org/C132519959","wikidata":"https://www.wikidata.org/wiki/Q3077373","display_name":"Test method","level":2,"score":0.33000001311302185},{"id":"https://openalex.org/C137209882","wikidata":"https://www.wikidata.org/wiki/Q1403517","display_name":"Measurement uncertainty","level":2,"score":0.32919999957084656},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.31459999084472656},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.31299999356269836},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.3118000030517578},{"id":"https://openalex.org/C66938386","wikidata":"https://www.wikidata.org/wiki/Q633538","display_name":"Structural engineering","level":1,"score":0.30979999899864197},{"id":"https://openalex.org/C135628077","wikidata":"https://www.wikidata.org/wiki/Q220184","display_name":"Finite element method","level":2,"score":0.3084999918937683},{"id":"https://openalex.org/C16910744","wikidata":"https://www.wikidata.org/wiki/Q7705759","display_name":"Test data","level":2,"score":0.298799991607666},{"id":"https://openalex.org/C2777267654","wikidata":"https://www.wikidata.org/wiki/Q3519023","display_name":"Test (biology)","level":2,"score":0.2935999929904938},{"id":"https://openalex.org/C128942645","wikidata":"https://www.wikidata.org/wiki/Q1568346","display_name":"Test case","level":3,"score":0.275299996137619},{"id":"https://openalex.org/C152745839","wikidata":"https://www.wikidata.org/wiki/Q5438153","display_name":"Fault detection and isolation","level":3,"score":0.27469998598098755},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.27459999918937683},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.27399998903274536},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.27129998803138733},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.26249998807907104},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.26170000433921814},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.25429999828338623},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.25130000710487366}],"mesh":[],"locations_count":1,"locations":[{"id":"pmh:oai:elib.dlr.de:226108","doi":null,"is_oa":false,"landing_page_url":"https://elib.dlr.de/226108/","pdf_url":null,"source":{"id":"https://openalex.org/S4377196266","display_name":"elib (German Aerospace Center)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I2898391981","host_organization_name":"Deutsches Zentrum f\u00fcr Luft- und Raumfahrt e. V. (DLR)","host_organization_lineage":["https://openalex.org/I2898391981"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Konferenzbeitrag"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","score":0.651199996471405,"display_name":"Industry, innovation and infrastructure"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W7213564754","https://openalex.org/W7204597652","https://openalex.org/W7118002673","https://openalex.org/W4390457524","https://openalex.org/W4411423517","https://openalex.org/W4415095606","https://openalex.org/W7133312488","https://openalex.org/W4220881967","https://openalex.org/W6962495796","https://openalex.org/W2944941262"],"abstract_inverted_index":{"The":[0,336,455,474,503,548,565],"design":[1,352],"and":[2,39,190,276,440,509],"certification":[3,52,184],"of":[4,54,112,153,171,192,212,221,259,290,354,381,391,405,424,466,500,521,554,559,579,602,630,636,657,672,675,680],"aero-composite":[5],"structures":[6,582],"rely":[7],"on":[8,23,247,408,427,438,452,518,540],"the":[9,15,34,46,51,66,110,113,118,146,150,154,188,202,219,227,238,245,257,286,297,301,307,332,351,370,378,388,392,400,403,409,422,428,434,441,447,458,467,478,491,494,498,501,507,510,519,522,538,557,576,600,609,616,654],"test":[10,26,35,42,114,155,239],"pyramid,":[11,240],"also":[12,295],"known":[13],"as":[14,29,165,175,215,226],"building":[16],"block":[17],"approach.":[18],"This":[19,116,157,281,375,525],"process":[20,53,185],"currently":[21],"depends":[22,517],"experimental-based":[24],"statistical":[25],"evidence":[27],"such":[28,174],"material":[30,198,210,316,596],"allowables":[31,629],"[1].":[32],"Thus,":[33],"pyramid":[36],"necessitates":[37],"costly":[38],"time-consuming":[40],"experimental":[41,248,320,542],"campaigns":[43],"up":[44],"to":[45,104,138,149,186,243,312,330,368,396,420,652,692],"full-scale":[47],"structure.":[48],"To":[49,128,356],"accelerate":[50],"fibre":[55],"reinforced":[56],"polymer":[57],"(FRP)":[58],"composite":[59,193,581,631],"structures,":[60],"there":[61],"is":[62,181,323,366,384,412,481,552],"growing":[63,131],"interest":[64],"in":[65,109,183,325,339,575,697],"aerospace":[67],"industry":[68],"for":[69,120,207,430,611,615],"integrating":[70],"machine":[71],"learning":[72],"(ML)":[73],"models,":[74],"which":[75],"can":[76,84,568,592],"shorten":[77],"development":[78],"cycles.":[79],"For":[80],"example,":[81],"surrogate":[82,100,142,262,342,360,393,435,460,479,546,550],"models":[83,101,512],"replace":[85],"computationally":[86],"expensive":[87],"finite":[88],"element":[89],"(FE)":[90],"simulations":[91,223],"with":[92,300,327,446,562],"an":[93,463],"inverse":[94],"inference":[95],"model":[96,143,343,394,410,431,436,443,461,480,495,551],"[2,3].":[97],"However,":[98],"standard":[99],"often":[102],"fail":[103],"capture":[105],"inherent":[106],"data":[107],"variability":[108],"context":[111],"pyramid.":[115,156],"highlights":[117],"need":[119],"implementing":[121],"probabilistic":[122,141,261,545],"methods":[123],"that":[124,144,265,349,377,514,536,589],"acknowledge":[125],"real-world":[126],"variations.":[127],"address":[129],"these":[130],"needs,":[132],"this":[133,196,606],"study":[134,158,168,505],"reports":[135],"a":[136,140,166,209,260,314,341,346,358,482,486,528,533,612],"framework":[137,282],"develop":[139],"connects":[145],"coupon":[147,203,252,308],"level":[148,152,204,309],"sub-element":[151],"uses":[159],"low":[160],"velocity":[161],"impact":[162,172,178,222,293,321,333,468,558],"(LVI)":[163],"testing":[164,322,543],"case":[167],"since":[169],"understanding":[170],"damages":[173],"barely":[176],"visible":[177],"damage":[179],"(BVID)":[180],"crucial":[182],"ensure":[187],"performance":[189,515],"safety":[191],"structures.":[194,677],"In":[195,399],"study,":[197],"properties":[199],"measured":[200],"at":[201,251,306,471],"are":[205,224,310,444],"used":[206],"constructing":[208],"card":[211],"FE":[213,334],"simulation":[214],"input":[216,449],"features,":[217],"while":[218,598],"results":[220,456],"served":[225],"target":[228],"parameters.":[229],"By":[230],"establishing":[231],"correlations":[232],"between":[233,506],"different":[234],"length":[235],"scales":[236],"within":[237,493],"we":[241],"aim":[242],"reduce":[244],"reliance":[246,539],"tests":[249,305],"conducted":[250,324],"level.":[253],"Fig.":[254],"1":[255],"presents":[256],"overview":[258],"modelling":[263],"approach":[264,535,614],"utilises":[266],"Bayesian":[267],"Neural":[268,698],"Networks":[269],"(BNNs)":[270],"via":[271],"Monte":[272],"Carlo":[273],"(MC)":[274],"Dropout":[275],"Gaussian":[277],"Process":[278],"Regression":[279],"(GPR).":[280],"not":[283],"only":[284],"predicts":[285],"energy":[287,470,561],"absorption":[288],"behaviour":[289],"CFRP":[291],"under":[292],"but":[294],"quantifies":[296],"uncertainty":[298,492,567,591],"associated":[299,563],"predictions.":[302,502],"Various":[303],"mechanical":[304],"performed":[311],"generate":[313],"LS-DYNA\u00ae":[315],"card,":[317],"MAT_ENHANCED_COMPOSITE_DAMAGE.":[318],"Subsequently,":[319],"accordance":[326],"ASTM":[328],"D7136/D7136M":[329],"validate":[331],"simulation.":[335],"first":[337,529],"step":[338,530],"training":[340,523],"involves":[344],"generating":[345],"comprehensive":[347],"dataset":[348],"encompasses":[350],"space":[353,373],"interest.":[355],"build":[357],"robust":[359],"model,":[361],"Latin":[362],"Hypercube":[363],"Sampling":[364],"(LHS)":[365],"implemented":[367],"cover":[369],"multidimensional":[371],"parameter":[372,383,407,426],"efficiently.":[374],"ensures":[376],"entire":[379],"range":[380],"each":[382,406,425],"sampled,":[385],"significantly":[386,516],"improving":[387],"convergence":[389],"rate":[390],"compared":[395],"random":[397],"sampling.":[398],"second":[401],"step,":[402],"contribution":[404],"prediction":[411,429,465,671],"investigated":[413],"using":[414],"SHapley":[415],"Additive":[416],"exPlanations":[417],"(SHAP)":[418],"[4]":[419,684],"find":[421],"importance":[423],"interpretability.":[432],"Finally,":[433],"based":[437,451,670],"BNN":[439],"GPR":[442,511],"trained":[445,459],"reduced":[448,472],"parameters":[450],"their":[453],"importance.":[454],"from":[457],"enable":[462],"accurate":[464],"residual":[469,560],"time-to-prediction.":[473],"output":[475],"generated":[476],"by":[477],"distribution":[483],"rather":[484],"than":[485],"single":[487],"deterministic":[488],"prediction.":[489],"Additionally,":[490],"helps":[496],"assess":[497],"confidence":[499],"comparative":[504],"BNNs":[508],"indicates":[513],"size":[520],"dataset.":[524],"work":[526,607],"represents":[527],"toward":[531],"developing":[532,580],"data-driven":[534],"minimises":[537],"physical":[541],"through":[544],"modelling.":[547],"proposed":[549],"capable":[553],"accurately":[555],"predicting":[556],"uncertainty.":[564],"identified":[566],"be":[569],"incorporated":[570],"into":[571],"decision-making":[572],"processes,":[573],"especially":[574],"early":[577],"stage":[578],"like":[583],"evaluating":[584],"operational":[585],"applicability.":[586],"Consequently,":[587],"predictions":[588],"include":[590],"help":[593],"identify":[594],"suitable":[595],"candidates":[597],"reducing":[599],"number":[601],"required":[603],"experiments.":[604],"Furthermore,":[605],"paves":[608],"way":[610],"scalable":[613],"reduction.":[617],"References:":[618],"[1]":[619],"Cumbo":[620],"R,":[621],"Baroni":[622],"A,":[623,625],"Ricciardi":[624],"et":[626,647],"al.":[627,648],"Design":[628],"laminates:":[632],"A":[633,689],"review.":[634],"Journal":[635,679],"Composite":[637],"Materials.":[638],"2022;56(23):3617-3634.":[639],"[2]":[640],"Arndt":[641],"C,":[642,644],"Crusenberry":[643],"Heng":[645],"B,":[646],"Reduced-Dimension":[649],"Surrogate":[650],"Modeling":[651],"Characterize":[653],"Damage":[655],"Tolerance":[656],"Composite/Metal":[658],"Structures.":[659],"Modelling.":[660],"2023;4(4):485-514.":[661],"[3]":[662],"Sakaridis":[663],"E,":[664],"Karathanasopoulos":[665],"N,":[666],"Mohr":[667],"D.":[668],"Machine-learning":[669],"crash":[673],"response":[674],"tubular":[676],"International":[678],"Impact":[681],"Engineering.":[682],"2022;166:104240.":[683],"Lundberg":[685],"SM,":[686],"Lee":[687],"S-I.":[688],"Unified":[690],"Approach":[691],"Interpreting":[693],"Model":[694],"Predictions.":[695],"Advances":[696],"Information":[699],"Processing":[700],"Systems.":[701],"2017;30.":[702]},"counts_by_year":[],"updated_date":"2026-10-28T10:33:24.094632","created_date":"2026-08-20T00:00:00"}
