{"id":"https://openalex.org/W4417455286","doi":"https://doi.org/10.1049/icp.2025.3495","title":"Real-time digital twin inference for melt pool dynamics using deep neural operators","display_name":"Real-time digital twin inference for melt pool dynamics using deep neural operators","publication_year":2025,"publication_date":"2025-12-01","ids":{"openalex":"https://openalex.org/W4417455286","doi":"https://doi.org/10.1049/icp.2025.3495"},"language":"en","primary_location":{"id":"doi:10.1049/icp.2025.3495","doi":"https://doi.org/10.1049/icp.2025.3495","is_oa":false,"landing_page_url":"https://doi.org/10.1049/icp.2025.3495","pdf_url":null,"source":{"id":"https://openalex.org/S4404663355","display_name":"IET conference proceedings.","issn_l":"2732-4494","issn":["2732-4494"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":["jufo-1"],"host_organization":"https://openalex.org/P4310311714","host_organization_name":"Institution of Engineering and Technology","host_organization_lineage":["https://openalex.org/P4310311714"],"host_organization_lineage_names":["Institution of Engineering and Technology"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IET Conference Proceedings","raw_type":"journal-article"},"volume":null,"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/A5101854208","display_name":"Hongyu Zhu","orcid":"https://orcid.org/0000-0003-2128-3938","observed_orcids":["https://orcid.org/0000-0003-2128-3938"]},"institutions":[{"id":"https://openalex.org/I27357992","display_name":"Dalian University of Technology","ror":"https://ror.org/023hj5876","country_code":"CN","type":"education","lineage":["https://openalex.org/I27357992"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongyu Zhu","raw_affiliation_strings":["School of Mechanical Engineering, Dalian University of Technology, Dalian 116024, People's Republic of China","State Key Laboratory of High-Performance Precision Manufacturing, Dalian 116024, People's Republic of China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Mechanical Engineering, Dalian University of Technology, Dalian 116024, People's Republic of China","institution_ids":["https://openalex.org/I27357992"]},{"raw_affiliation_string":"State Key Laboratory of High-Performance Precision Manufacturing, Dalian 116024, People's Republic of China","institution_ids":[]}]},{"author_position":"middle","author":{"id":null,"display_name":"Fuwen Liu","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I27357992","display_name":"Dalian University of Technology","ror":"https://ror.org/023hj5876","country_code":"CN","type":"education","lineage":["https://openalex.org/I27357992"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fuwen Liu","raw_affiliation_strings":["School of Mechanical Engineering, Dalian University of Technology, Dalian 116024, People's Republic of China","State Key Laboratory of High-Performance Precision Manufacturing, Dalian 116024, People's Republic of China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Mechanical Engineering, Dalian University of Technology, Dalian 116024, People's Republic of China","institution_ids":["https://openalex.org/I27357992"]},{"raw_affiliation_string":"State Key Laboratory of High-Performance Precision Manufacturing, Dalian 116024, People's Republic of China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027707683","display_name":"Xiwang He","orcid":"https://orcid.org/0000-0002-0194-4930","observed_orcids":["https://orcid.org/0000-0002-0194-4930"]},"institutions":[{"id":"https://openalex.org/I27357992","display_name":"Dalian University of Technology","ror":"https://ror.org/023hj5876","country_code":"CN","type":"education","lineage":["https://openalex.org/I27357992"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiwang He","raw_affiliation_strings":["School of Mechanical Engineering, Dalian University of Technology, Dalian 116024, People's Republic of China","State Key Laboratory of High-Performance Precision Manufacturing, Dalian 116024, People's Republic of China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Mechanical Engineering, Dalian University of Technology, Dalian 116024, People's Republic of China","institution_ids":["https://openalex.org/I27357992"]},{"raw_affiliation_string":"State Key Laboratory of High-Performance Precision Manufacturing, Dalian 116024, People's Republic of China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5071611991","display_name":"Xueguan Song","orcid":"https://orcid.org/0000-0002-8235-5870","observed_orcids":["https://orcid.org/0000-0002-8235-5870"]},"institutions":[{"id":"https://openalex.org/I27357992","display_name":"Dalian University of Technology","ror":"https://ror.org/023hj5876","country_code":"CN","type":"education","lineage":["https://openalex.org/I27357992"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xueguan Song","raw_affiliation_strings":["School of Mechanical Engineering, Dalian University of Technology, Dalian 116024, People's Republic of China","State Key Laboratory of High-Performance Precision Manufacturing, Dalian 116024, People's Republic of China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Mechanical Engineering, Dalian University of Technology, Dalian 116024, People's Republic of China","institution_ids":["https://openalex.org/I27357992"]},{"raw_affiliation_string":"State Key Laboratory of High-Performance Precision Manufacturing, Dalian 116024, People's Republic of China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I27357992"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.47465289,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"2025","issue":"35","first_page":"804","last_page":"812"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11206","display_name":"Model Reduction and Neural Networks","score":0.9878000020980835,"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":0.9878000020980835,"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/T10705","display_name":"Additive Manufacturing Materials and Processes","score":0.007799999788403511,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical Engineering"},"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/T10320","display_name":"Neural Networks and Applications","score":0.0005000000237487257,"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/melt-pool-dynamics","display_name":"melt pool dynamics","score":0.9760000109672546},{"id":"https://openalex.org/keywords/metal-additive-manufacturing","display_name":"metal additive manufacturing","score":0.843999981880188},{"id":"https://openalex.org/keywords/digital-twin","display_name":"digital twin","score":0.925000011920929},{"id":"https://openalex.org/keywords/deeponet","display_name":"DeepONet","score":0.7549999952316284},{"id":"https://openalex.org/keywords/surrogate-modeling","display_name":"surrogate models","score":0.7080000042915344},{"id":"https://openalex.org/keywords/soft-sensors","display_name":"soft sensors","score":0.9160000085830688},{"id":"https://openalex.org/keywords/melt-pool-monitoring","display_name":"melt pool monitoring","score":0.671999990940094},{"id":"https://openalex.org/keywords/physics-informed-neural-networks","display_name":"physics-informed neural networks","score":0.777999997138977}],"concepts":[{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6886000037193298},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5805000066757202},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5799000263214111},{"id":"https://openalex.org/C177606310","wikidata":"https://www.wikidata.org/wiki/Q5674297","display_name":"Adaptability","level":2,"score":0.46779999136924744},{"id":"https://openalex.org/C17020691","wikidata":"https://www.wikidata.org/wiki/Q139677","display_name":"Operator (biology)","level":5,"score":0.46549999713897705},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4163999855518341},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4120999872684479},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.39329999685287476},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.3864000141620636},{"id":"https://openalex.org/C1633027","wikidata":"https://www.wikidata.org/wiki/Q815820","display_name":"Computational fluid dynamics","level":2,"score":0.3257000148296356},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.32280001044273376},{"id":"https://openalex.org/C182310444","wikidata":"https://www.wikidata.org/wiki/Q1332643","display_name":"Boundary value problem","level":2,"score":0.3221000134944916},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.3199999928474426},{"id":"https://openalex.org/C172205157","wikidata":"https://www.wikidata.org/wiki/Q1782962","display_name":"Model predictive control","level":3,"score":0.31279999017715454},{"id":"https://openalex.org/C66024118","wikidata":"https://www.wikidata.org/wiki/Q1122506","display_name":"Computational model","level":2,"score":0.2847999930381775},{"id":"https://openalex.org/C2780598303","wikidata":"https://www.wikidata.org/wiki/Q65921492","display_name":"Flexibility (engineering)","level":2,"score":0.2705000042915344},{"id":"https://openalex.org/C123757187","wikidata":"https://www.wikidata.org/wiki/Q9195957","display_name":"Network dynamics","level":2,"score":0.2669000029563904},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.26570001244544983},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2653999924659729},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.2648000121116638},{"id":"https://openalex.org/C133731056","wikidata":"https://www.wikidata.org/wiki/Q4917288","display_name":"Control engineering","level":1,"score":0.26010000705718994}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1049/icp.2025.3495","doi":"https://doi.org/10.1049/icp.2025.3495","is_oa":false,"landing_page_url":"https://doi.org/10.1049/icp.2025.3495","pdf_url":null,"source":{"id":"https://openalex.org/S4404663355","display_name":"IET conference proceedings.","issn_l":"2732-4494","issn":["2732-4494"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":["jufo-1"],"host_organization":"https://openalex.org/P4310311714","host_organization_name":"Institution of Engineering and Technology","host_organization_lineage":["https://openalex.org/P4310311714"],"host_organization_lineage_names":["Institution of Engineering and Technology"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IET Conference Proceedings","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.5378999710083008}],"sustainable_development_goals_aurora":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"This":[0,68],"study":[1],"explores":[2],"melt":[3,86,159],"pool":[4,87,160],"dynamics":[5,37],"in":[6,26,134,162],"metal":[7],"additive":[8,163],"manufacturing,":[9],"which":[10],"serve":[11],"as":[12,34,76,78,120,152],"a":[13,52,79,144,153,168],"critical":[14],"determinant":[15],"of":[16,118,146],"microstructure":[17],"evolution,":[18],"material":[19,58],"uniformity,":[20],"process":[21,105],"stability,":[22],"and":[23,38,100,107,137,174],"defect":[24],"mitigation":[25],"fabricated":[27],"components.":[28],"Although":[29],"conventional":[30],"numerical":[31],"methods":[32],"such":[33],"computational":[35,49,172],"fluid":[36],"emerging":[39],"physics-informed":[40],"neural":[41],"networks":[42],"have":[43],"demonstrated":[44],"predictive":[45,112],"capabilities,":[46],"their":[47],"substantial":[48],"cost":[50],"presents":[51,70],"significant":[53],"constraint,":[54],"particularly":[55],"under":[56],"varying":[57],"properties":[59],"or":[60],"boundary":[61],"conditions":[62,106],"that":[63],"necessitate":[64],"costly":[65],"retraining":[66],"procedures.":[67],"investigation":[69],"the":[71,129],"Deep":[72],"Operator":[73],"Network,":[74],"denoted":[75],"DeepONet,":[77],"computationally":[80],"efficient":[81],"surrogate":[82],"model":[83,95],"for":[84,158],"real-time":[85],"prediction":[88],"within":[89],"digital":[90,165],"twin":[91],"frameworks.":[92],"The":[93],"proposed":[94],"accurately":[96],"captures":[97],"velocity,":[98],"pressure,":[99],"temperature":[101],"fields":[102],"across":[103,143],"diverse":[104],"time":[108],"increments,":[109],"achieving":[110],"high":[111],"performance":[113],"with":[114],"an":[115],"R\u00b2":[116],"value":[117],"0.99,":[119],"verified":[121],"through":[122],"extensive":[123],"benchmarking.":[124],"Ablation":[125],"studies":[126],"further":[127],"confirm":[128],"model's":[130],"robustness":[131],"to":[132],"variations":[133],"network":[135],"depth":[136],"neuron":[138],"configuration,":[139],"underscoring":[140],"its":[141],"adaptability":[142],"range":[145],"scenarios.":[147],"These":[148],"attributes":[149],"establish":[150],"DeepONet":[151],"highly":[154],"effective":[155],"virtual":[156],"sensor":[157],"monitoring":[161],"manufacturing":[164],"twins,":[166],"offering":[167],"favorable":[169],"balance":[170],"between":[171],"efficiency":[173],"physical":[175],"fidelity.":[176]},"counts_by_year":[],"updated_date":"2026-09-27T08:07:27.782135","created_date":"2025-12-18T00:00:00"}