{"id":"https://openalex.org/W7160842562","doi":"https://doi.org/10.48550/arxiv.2605.07098","title":"CarCrashNet: A Large-Scale Dataset and Hierarchical Neural Solver for Data-Driven Structural Crash Simulation","display_name":"CarCrashNet: A Large-Scale Dataset and Hierarchical Neural Solver for Data-Driven Structural Crash Simulation","publication_year":2026,"publication_date":"2026-05-08","ids":{"openalex":"https://openalex.org/W7160842562","doi":"https://doi.org/10.48550/arxiv.2605.07098"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.07098","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.07098","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.2605.07098","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5135103571","display_name":"Mohamed Elrefaie","orcid":"https://orcid.org/0009-0008-9981-0930"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Elrefaie, Mohamed","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135852715","display_name":"Dule Shu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shu, Dule","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135860175","display_name":"Matthew Klenk","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Klenk, Matthew","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5135884913","display_name":"Faez Ahmed","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ahmed, Faez","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":0.5008000135421753,"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":0.5008000135421753,"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/T10848","display_name":"Advanced Multi-Objective Optimization Algorithms","score":0.10610000044107437,"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"}},{"id":"https://openalex.org/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.0430000014603138,"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/crash","display_name":"Crash","score":0.8176000118255615},{"id":"https://openalex.org/keywords/crashworthiness","display_name":"Crashworthiness","score":0.684499979019165},{"id":"https://openalex.org/keywords/solver","display_name":"Solver","score":0.6222000122070312},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5618000030517578},{"id":"https://openalex.org/keywords/workflow","display_name":"Workflow","score":0.48489999771118164},{"id":"https://openalex.org/keywords/benchmarking","display_name":"Benchmarking","score":0.42489999532699585},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.367900013923645}],"concepts":[{"id":"https://openalex.org/C183469790","wikidata":"https://www.wikidata.org/wiki/Q333501","display_name":"Crash","level":2,"score":0.8176000118255615},{"id":"https://openalex.org/C2779240047","wikidata":"https://www.wikidata.org/wiki/Q3283022","display_name":"Crashworthiness","level":3,"score":0.684499979019165},{"id":"https://openalex.org/C2778770139","wikidata":"https://www.wikidata.org/wiki/Q1966904","display_name":"Solver","level":2,"score":0.6222000122070312},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5634999871253967},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5618000030517578},{"id":"https://openalex.org/C177212765","wikidata":"https://www.wikidata.org/wiki/Q627335","display_name":"Workflow","level":2,"score":0.48489999771118164},{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.42489999532699585},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.367900013923645},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.3650999963283539},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.3402000069618225},{"id":"https://openalex.org/C171268870","wikidata":"https://www.wikidata.org/wiki/Q1486676","display_name":"GRASP","level":2,"score":0.33709999918937683},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3100000023841858},{"id":"https://openalex.org/C198082294","wikidata":"https://www.wikidata.org/wiki/Q3399648","display_name":"Position (finance)","level":2,"score":0.3043999969959259},{"id":"https://openalex.org/C51175575","wikidata":"https://www.wikidata.org/wiki/Q431790","display_name":"Crash test","level":3,"score":0.2953000068664551},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2946000099182129},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.2897999882698059},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2872999906539917},{"id":"https://openalex.org/C2780719617","wikidata":"https://www.wikidata.org/wiki/Q1030752","display_name":"Salient","level":2,"score":0.2775999903678894},{"id":"https://openalex.org/C44154836","wikidata":"https://www.wikidata.org/wiki/Q45045","display_name":"Simulation","level":1,"score":0.2603999972343445},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.25920000672340393},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2515000104904175}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.07098","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.07098","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.2605.07098","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.07098","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":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.4658784866333008}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Crash":[0],"simulation":[1],"is":[2,41,214],"a":[3,70,87,158,197],"cornerstone":[4],"of":[5,116,131],"modern":[6],"vehicle":[7,114],"development":[8],"because":[9],"it":[10],"reduces":[11],"the":[12,29,39,129,132,149,177],"need":[13],"for":[14,75,162,199],"costly":[15],"physical":[16],"prototypes,":[17],"accelerates":[18],"safety-driven":[19],"design":[20],"iteration,":[21],"and":[22,51,58,83,101,124,148,180,188,207],"increasingly":[23],"supports":[24],"virtual":[25,209],"testing":[26],"workflows.":[27],"At":[28],"same":[30],"time,":[31],"modeling":[32],"structural":[33,77,118,203],"crash":[34,78,108,146,164,169,210],"mechanics":[35],"remains":[36],"exceptionally":[37],"challenging:":[38],"response":[40],"governed":[42],"by":[43],"nonlinear":[44],"contact,":[45],"large":[46],"deformation,":[47],"material":[48],"plasticity,":[49],"failure,":[50],"complex":[52],"multi-body":[53],"interactions":[54],"evolving":[55],"over":[56],"space":[57],"time":[59],"on":[60,141],"high-resolution":[61,167],"finite-element":[62,138,168],"meshes.":[63],"In":[64],"this":[65],"work,":[66],"we":[67,134],"introduce":[68,156],"CarCrashNet,":[69],"public":[71],"high-fidelity":[72],"open-source":[73,137],"benchmark":[74],"data-driven":[76],"simulation.":[79],"CarCrashNet":[80,195],"combines":[81],"component-scale":[82],"full-vehicle":[84,107,163],"simulations":[85,96,109],"in":[86,202],"multi-modal":[88],"format,":[89],"including":[90],"more":[91],"than":[92],"14,000":[93],"bumper-beam":[94],"pole-impact":[95],"with":[97,105],"varying":[98],"geometry,":[99],"materials,":[100],"boundary":[102],"conditions,":[103],"together":[104],"825":[106],"built":[110],"from":[111,166],"three":[112],"industry-standard":[113],"models":[115],"increasing":[117],"complexity:":[119],"Dodge":[120],"Neon,":[121],"Toyota":[122],"Yaris,":[123],"Chevrolet":[125],"Silverado.":[126],"To":[127],"establish":[128],"reliability":[130],"benchmark,":[133],"validate":[135],"our":[136],"workflow":[139],"based":[140],"OpenRadioss":[142],"against":[143,183],"both":[144],"experimental":[145],"data":[147],"commercial":[150],"solver":[151],"Ansys":[152],"LS-DYNA.":[153],"We":[154,171],"also":[155],"CrashSolver,":[157],"machine-learning":[159],"model":[160],"designed":[161],"prediction":[165],"data.":[170],"further":[172],"perform":[173],"extensive":[174],"benchmarking":[175],"across":[176],"released":[178],"datasets":[179],"evaluate":[181],"CrashSolver":[182],"state-of-the-art":[184],"geometric":[185],"deep":[186],"learning":[187],"transformer-based":[189],"neural":[190],"solvers.":[191],"Our":[192],"results":[193],"position":[194],"as":[196],"foundation":[198],"reproducible":[200],"research":[201],"simulation,":[204],"crashworthiness":[205],"modeling,":[206],"AI-driven":[208],"testing.":[211],"The":[212],"dataset":[213],"available":[215],"at":[216],"https://github.com/Mohamedelrefaie/CarCrashNet.":[217]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-12T00:00:00"}