{"id":"https://openalex.org/W7162640856","doi":"https://doi.org/10.48550/arxiv.2605.27758","title":"High-Fidelity Industrial Crash Dynamics Prediction via Geometry-Aware Operator Learning with Memory-Efficient Low-Rank Attention","display_name":"High-Fidelity Industrial Crash Dynamics Prediction via Geometry-Aware Operator Learning with Memory-Efficient Low-Rank Attention","publication_year":2026,"publication_date":"2026-05-26","ids":{"openalex":"https://openalex.org/W7162640856","doi":"https://doi.org/10.48550/arxiv.2605.27758"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.27758","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.27758","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.2605.27758","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5137266560","display_name":"Deepak Akhare","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Akhare, Deepak","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012913879","display_name":"Mohammad Amin Nabian","orcid":"https://orcid.org/0000-0003-1824-4217"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nabian, Mohammad Amin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5121261821","display_name":"Corey Adams","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Adams, Corey","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137258085","display_name":"Sudeep Chavare","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chavare, Sudeep","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5137240872","display_name":"Sanjay Choudhry","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Choudhry, Sanjay","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"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/T11487","display_name":"Automotive and Human Injury Biomechanics","score":0.17829999327659607,"subfield":{"id":"https://openalex.org/subfields/2740","display_name":"Pulmonary and Respiratory Medicine"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T11487","display_name":"Automotive and Human Injury Biomechanics","score":0.17829999327659607,"subfield":{"id":"https://openalex.org/subfields/2740","display_name":"Pulmonary and Respiratory Medicine"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T11206","display_name":"Model Reduction and Neural Networks","score":0.15399999916553497,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.07620000094175339,"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/crashworthiness","display_name":"Crashworthiness","score":0.6304000020027161},{"id":"https://openalex.org/keywords/automotive-industry","display_name":"Automotive industry","score":0.5907999873161316},{"id":"https://openalex.org/keywords/crash","display_name":"Crash","score":0.5354999899864197},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.5220999717712402},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5148000121116638},{"id":"https://openalex.org/keywords/operator","display_name":"Operator (biology)","score":0.45320001244544983}],"concepts":[{"id":"https://openalex.org/C2779240047","wikidata":"https://www.wikidata.org/wiki/Q3283022","display_name":"Crashworthiness","level":3,"score":0.6304000020027161},{"id":"https://openalex.org/C526921623","wikidata":"https://www.wikidata.org/wiki/Q190117","display_name":"Automotive industry","level":2,"score":0.5907999873161316},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5648000240325928},{"id":"https://openalex.org/C183469790","wikidata":"https://www.wikidata.org/wiki/Q333501","display_name":"Crash","level":2,"score":0.5354999899864197},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.5220999717712402},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5148000121116638},{"id":"https://openalex.org/C17020691","wikidata":"https://www.wikidata.org/wiki/Q139677","display_name":"Operator (biology)","level":5,"score":0.45320001244544983},{"id":"https://openalex.org/C131675550","wikidata":"https://www.wikidata.org/wiki/Q7646884","display_name":"Surrogate model","level":2,"score":0.4142000079154968},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.39750000834465027},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.36899998784065247},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36730000376701355},{"id":"https://openalex.org/C79581498","wikidata":"https://www.wikidata.org/wiki/Q1367530","display_name":"Suite","level":2,"score":0.35519999265670776},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.3370000123977661},{"id":"https://openalex.org/C117896860","wikidata":"https://www.wikidata.org/wiki/Q11376","display_name":"Acceleration","level":2,"score":0.29649999737739563},{"id":"https://openalex.org/C79487989","wikidata":"https://www.wikidata.org/wiki/Q934680","display_name":"Vehicle dynamics","level":2,"score":0.29030001163482666},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.2840999960899353},{"id":"https://openalex.org/C133731056","wikidata":"https://www.wikidata.org/wiki/Q4917288","display_name":"Control engineering","level":1,"score":0.2587999999523163}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.27758","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.27758","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.2605.27758","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.27758","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":[{"id":"https://metadata.un.org/sdg/7","score":0.4077394902706146,"display_name":"Affordable and clean energy"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Automotive":[0],"crashworthiness":[1],"optimization":[2],"remains":[3,56],"a":[4,70,120,150,155],"safety-critical":[5,210],"challenge,":[6],"requiring":[7],"the":[8,66,112,135,164,186,191,197],"management":[9],"of":[10,122,190,200,208],"large-scale":[11],"nonlinear":[12],"structural":[13],"deformations":[14],"and":[15,50,87,96,117,129,146],"energy":[16],"dissipation":[17],"through":[18],"iterative,":[19],"high-fidelity":[20,75,205],"simulations.":[21],"While":[22],"traditional":[23],"finite":[24],"element":[25],"solvers":[26],"are":[27],"computationally":[28],"prohibitive,":[29],"emerging":[30],"operator":[31,202],"learning":[32,203],"frameworks":[33],"provide":[34],"rapid":[35],"surrogate":[36,206],"predictions;":[37],"however,":[38],"applying":[39],"them":[40],"to":[41,163],"industrial-scale":[42],"crash":[43,76,89],"analysis,":[44],"where":[45],"complex":[46,84],"geometry,":[47],"contact":[48],"nonlinearities,":[49],"rapidly":[51],"evolving":[52],"transient":[53],"deformation":[54,100],"coexist,":[55],"an":[57],"open":[58],"challenge.":[59],"In":[60],"this":[61],"paper,":[62],"we":[63,115,153],"demonstrate":[64],"that":[65,134,168],"GeoTransolver":[67,91,165],"framework":[68],"provides":[69],"viable":[71],"solution":[72],"for":[73,180,204],"accurate,":[74],"dynamics":[77],"prediction":[78,124],"at":[79,107],"industrial":[80],"scale.":[81],"Benchmarked":[82],"on":[83],"bumper":[85],"beam":[86],"full-vehicle":[88],"datasets,":[90],"captures":[92],"multi-scale":[93],"geometric":[94],"context":[95],"accurately":[97],"resolves":[98],"plastic":[99],"patterns":[101],"as":[102,104],"well":[103],"acceleration":[105],"profiles":[106],"critical":[108],"occupant":[109],"locations.":[110],"Beyond":[111],"architecture":[113],"itself,":[114],"propose":[116],"systematically":[118],"evaluate":[119],"suite":[121],"temporal":[123],"recipes,":[125],"including":[126],"one-shot,":[127],"time-conditional,":[128],"autoregressive":[130],"rollout":[131],"strategies,":[132],"demonstrating":[133],"one-shot":[136],"approach":[137],"achieves":[138],"state-of-the-art":[139],"accuracy":[140,179],"with":[141],"significantly":[142],"reduced":[143],"training":[144],"overhead":[145,171],"inference":[147],"latency.":[148],"As":[149],"secondary":[151],"contribution,":[152],"introduce":[154],"Fast":[156],"Low-rank":[157],"Attention":[158],"Routing":[159],"Engine":[160],"(FLARE)-based":[161],"modification":[162],"attention":[166],"backbone":[167],"reduces":[169],"memory":[170],"by":[172],"approximately":[173],"2x":[174],"while":[175],"further":[176],"improving":[177],"predictive":[178],"O(N)":[181],"long-range,":[182],"high-frequency":[183],"transients,":[184],"preserving":[185],"geometry-aware":[187,201],"cross-attention":[188],"strengths":[189],"base":[192],"framework.":[193],"Our":[194],"results":[195],"highlight":[196],"practical":[198],"viability":[199],"modeling":[207],"complex,":[209],"automotive":[211],"dynamics.":[212]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-05-29T00:00:00"}
