{"id":"https://openalex.org/W4412876925","doi":"https://doi.org/10.1145/3711896.3737238","title":"LaDEEP: A Deep Learning-based Surrogate Model for Large Deformation of Elastic-Plastic Solids","display_name":"LaDEEP: A Deep Learning-based Surrogate Model for Large Deformation of Elastic-Plastic Solids","publication_year":2025,"publication_date":"2025-08-03","ids":{"openalex":"https://openalex.org/W4412876925","doi":"https://doi.org/10.1145/3711896.3737238"},"language":"en","primary_location":{"id":"doi:10.1145/3711896.3737238","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3711896.3737238","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3711896.3737238","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3711896.3737238","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Shilong Tao","orcid":"https://orcid.org/0009-0003-2497-3413"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shilong Tao","raw_affiliation_strings":["School of Computer Science, Peking University, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0003-2497-3413","affiliations":[{"raw_affiliation_string":"School of Computer Science, Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Zhe Feng","orcid":"https://orcid.org/0009-0000-8682-3769"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhe Feng","raw_affiliation_strings":["School of Computer Science, Peking University, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0000-8682-3769","affiliations":[{"raw_affiliation_string":"School of Computer Science, Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Haonan Sun","orcid":"https://orcid.org/0009-0007-8784-6963"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haonan Sun","raw_affiliation_strings":["School of Computer Science, Peking University, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0007-8784-6963","affiliations":[{"raw_affiliation_string":"School of Computer Science, Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5045305860","display_name":"Zhanxing Zhu","orcid":"https://orcid.org/0000-0002-2141-6553"},"institutions":[{"id":"https://openalex.org/I43439940","display_name":"University of Southampton","ror":"https://ror.org/01ryk1543","country_code":"GB","type":"education","lineage":["https://openalex.org/I43439940"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Zhanxing Zhu","raw_affiliation_strings":["School of Electrical and Computer Science, University of Southampton, Southampton, United Kingdom"],"raw_orcid":"https://orcid.org/0000-0002-2141-6553","affiliations":[{"raw_affiliation_string":"School of Electrical and Computer Science, University of Southampton, Southampton, United Kingdom","institution_ids":["https://openalex.org/I43439940"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5082653046","display_name":"Yunhuai Liu","orcid":"https://orcid.org/0000-0002-1180-8078"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yunhuai Liu","raw_affiliation_strings":["School of Computer Science, Peking University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-1180-8078","affiliations":[{"raw_affiliation_string":"School of Computer Science, Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.13099259,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"4901","last_page":"4912"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11206","display_name":"Model Reduction and Neural Networks","score":0.996399998664856,"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":0.996399998664856,"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/T11948","display_name":"Machine Learning in Materials Science","score":0.9962000250816345,"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"}},{"id":"https://openalex.org/T11201","display_name":"Metallurgy and Material Forming","score":0.989799976348877,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/deformation","display_name":"Deformation (meteorology)","score":0.5811970233917236},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4941735565662384},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4692136347293854},{"id":"https://openalex.org/keywords/materials-science","display_name":"Materials science","score":0.40222448110580444},{"id":"https://openalex.org/keywords/composite-material","display_name":"Composite material","score":0.27481257915496826}],"concepts":[{"id":"https://openalex.org/C204366326","wikidata":"https://www.wikidata.org/wiki/Q3027650","display_name":"Deformation (meteorology)","level":2,"score":0.5811970233917236},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4941735565662384},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4692136347293854},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.40222448110580444},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.27481257915496826}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1145/3711896.3737238","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3711896.3737238","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3711896.3737238","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2","raw_type":"proceedings-article"},{"id":"pmh:oai:eprints.soton.ac.uk:507301","is_oa":false,"landing_page_url":"http://doi.org/10.1145/3711896.3737238>).","pdf_url":null,"source":{"id":"https://openalex.org/S4306401019","display_name":"ePrints Soton (University of Southampton)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I43439940","host_organization_name":"University of Southampton","host_organization_lineage":["https://openalex.org/I43439940"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":null,"raw_type":"PeerReviewed"},{"id":"pmh:oai:arXiv.org:2506.06001","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2506.06001","pdf_url":"https://arxiv.org/pdf/2506.06001","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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":"text"}],"best_oa_location":{"id":"doi:10.1145/3711896.3737238","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3711896.3737238","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3711896.3737238","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4412876925.pdf","grobid_xml":"https://content.openalex.org/works/W4412876925.grobid-xml"},"referenced_works_count":29,"referenced_works":["https://openalex.org/W612879516","https://openalex.org/W1558990301","https://openalex.org/W1912930600","https://openalex.org/W1980547124","https://openalex.org/W2023381719","https://openalex.org/W2033979036","https://openalex.org/W2045192107","https://openalex.org/W2082703072","https://openalex.org/W2139923370","https://openalex.org/W2194775991","https://openalex.org/W2515505748","https://openalex.org/W2794284562","https://openalex.org/W2810878077","https://openalex.org/W2899283552","https://openalex.org/W2990138404","https://openalex.org/W3137392741","https://openalex.org/W3138516171","https://openalex.org/W3197308578","https://openalex.org/W4213055646","https://openalex.org/W4236965008","https://openalex.org/W4285429067","https://openalex.org/W4300507242","https://openalex.org/W4301437120","https://openalex.org/W4317493021","https://openalex.org/W4392983051","https://openalex.org/W4399151268","https://openalex.org/W4415279017","https://openalex.org/W6600005384","https://openalex.org/W6600175564"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W4404995717","https://openalex.org/W2016187641","https://openalex.org/W4404725684","https://openalex.org/W4246450666","https://openalex.org/W4388998267","https://openalex.org/W4409278740","https://openalex.org/W2898370298"],"abstract_inverted_index":{"Scientific":[0],"computing":[1],"for":[2,10,62,108],"large":[3],"deformation":[4,154],"of":[5,113,121,141,158],"elastic-plastic":[6,77],"solids":[7,78,125],"is":[8,149,214],"critical":[9],"numerous":[11],"real-world":[12,198],"applications.":[13],"Classical":[14],"numerical":[15],"solvers":[16],"rely":[17],"primarily":[18],"on":[19,182],"local":[20],"discrete":[21],"linear":[22],"approximation":[23],"and":[24,33,55,70,82,100,177,202,211],"are":[25,60],"constrained":[26],"by":[27],"an":[28],"inherent":[29],"trade-off":[30],"between":[31],"accuracy":[32,210],"efficiency.":[34,212],"Recently,":[35],"deep":[36,104,187],"learning":[37,188],"models":[38,50],"have":[39,51,191],"achieved":[40],"impressive":[41],"progress":[42],"in":[43,208],"solving":[44],"the":[45,118,122,138,142,153,156],"continuum":[46],"mechanism.":[47],"While":[48],"previous":[49],"explored":[52],"various":[53],"architectures":[54],"constructed":[56],"coefficient-solution":[57],"mappings,":[58],"they":[59],"designed":[61,150],"general":[63],"instances":[64],"without":[65],"considering":[66],"specific":[67],"problem":[68],"properties":[69],"hard":[71],"to":[72,130,151,185],"accurately":[73],"handle":[74],"with":[75,155,173],"complex":[76],"involving":[79],"contact,":[80],"loading":[81],"unloading.":[83],"In":[84],"this":[85],"work,":[86],"we":[87],"take":[88],"stretch":[89],"bending,":[90],"a":[91,103,127,145,174,197],"popular":[92],"metal":[93],"fabrication":[94],"technique,":[95],"as":[96,160],"our":[97,194],"case":[98],"study":[99],"introduce":[101],"LaDEEP,":[102],"learning-based":[105],"surrogate":[106],"model":[107,195],"La":[109],"rge":[110],"De":[111],"formation":[112],"Elastic-Plastic":[114],"Solids.":[115],"We":[116,190],"encode":[117],"partitioned":[119],"regions":[120],"involved":[123],"slender":[124],"into":[126,196],"token":[128],"sequence":[129,157],"maintain":[131],"their":[132],"essential":[133],"order":[134],"property.":[135],"To":[136],"characterize":[137],"physical":[139],"process":[140],"solid":[143],"deformation,":[144],"two-stage":[146],"Transformer-based":[147],"module":[148],"predict":[152],"tokens":[159],"input.":[161],"Empirically,":[162],"LaDEEP":[163],"achieves":[164],"five":[165],"magnitudes":[166],"faster":[167],"speed":[168],"than":[169],"finite":[170],"element":[171],"methods":[172],"comparable":[175],"accuracy,":[176],"gains":[178],"20.47%":[179],"relative":[180],"improvement":[181],"average":[183],"compared":[184],"other":[186],"baselines.":[189],"also":[192],"deployed":[193],"industrial":[199],"production":[200],"system,":[201],"it":[203],"has":[204],"shown":[205],"remarkable":[206],"performance":[207],"both":[209],"Code":[213],"available":[215],"at":[216],"https://github.com/therontau0054/LaDEEP.":[217]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}