{"id":"https://openalex.org/W7154709746","doi":"https://doi.org/10.48550/arxiv.2604.14562","title":"Material-agnostic temperature field prediction for metal additive manufacturing via a parametric PINN framework","display_name":"Material-agnostic temperature field prediction for metal additive manufacturing via a parametric PINN framework","publication_year":2026,"publication_date":"2026-04-16","ids":{"openalex":"https://openalex.org/W7154709746","doi":"https://doi.org/10.48550/arxiv.2604.14562"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.14562","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.14562","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.14562","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5133915195","display_name":"Hyeonsu Lee","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lee, Hyeonsu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5133837824","display_name":"Jihoon Jeong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jeong, Jihoon","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/T10705","display_name":"Additive Manufacturing Materials and Processes","score":0.9650999903678894,"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"}},"topics":[{"id":"https://openalex.org/T10705","display_name":"Additive Manufacturing Materials and Processes","score":0.9650999903678894,"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/T11143","display_name":"High Entropy Alloys Studies","score":0.006099999882280827,"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/T11948","display_name":"Machine Learning in Materials Science","score":0.003700000001117587,"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/parametric-statistics","display_name":"Parametric statistics","score":0.6251000165939331},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.6186000108718872},{"id":"https://openalex.org/keywords/reduction","display_name":"Reduction (mathematics)","score":0.5371000170707703},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.5023000240325928},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.49300000071525574},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4616999924182892},{"id":"https://openalex.org/keywords/thermal","display_name":"Thermal","score":0.3928999900817871},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.3831000030040741},{"id":"https://openalex.org/keywords/scaling","display_name":"Scaling","score":0.36890000104904175}],"concepts":[{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.6251000165939331},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.6186000108718872},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5444999933242798},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.5371000170707703},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.5023000240325928},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.49300000071525574},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4616999924182892},{"id":"https://openalex.org/C204530211","wikidata":"https://www.wikidata.org/wiki/Q752823","display_name":"Thermal","level":2,"score":0.3928999900817871},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.3831000030040741},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.36890000104904175},{"id":"https://openalex.org/C62354387","wikidata":"https://www.wikidata.org/wiki/Q875399","display_name":"Boundary (topology)","level":2,"score":0.3628000020980835},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.36149999499320984},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.3560999929904938},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.3481999933719635},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3443000018596649},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3285999894142151},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.32659998536109924},{"id":"https://openalex.org/C42747912","wikidata":"https://www.wikidata.org/wiki/Q1048447","display_name":"Multiplicative function","level":2,"score":0.3188999891281128},{"id":"https://openalex.org/C27158222","wikidata":"https://www.wikidata.org/wiki/Q5532422","display_name":"Generalizability theory","level":2,"score":0.31150001287460327},{"id":"https://openalex.org/C24574437","wikidata":"https://www.wikidata.org/wiki/Q7135228","display_name":"Parametric model","level":3,"score":0.30489999055862427},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.3043000102043152},{"id":"https://openalex.org/C2776036281","wikidata":"https://www.wikidata.org/wiki/Q48769818","display_name":"Constraint (computer-aided design)","level":2,"score":0.29750001430511475},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.2761000096797943},{"id":"https://openalex.org/C122383733","wikidata":"https://www.wikidata.org/wiki/Q865920","display_name":"Approximation error","level":2,"score":0.2533999979496002},{"id":"https://openalex.org/C2780598303","wikidata":"https://www.wikidata.org/wiki/Q65921492","display_name":"Flexibility (engineering)","level":2,"score":0.25119999051094055}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.14562","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.14562","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.14562","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.14562","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Accurate":[0],"temperature":[1,218],"field":[2,219],"prediction":[3],"in":[4,105,168,202,228],"metal":[5,142,229],"additive":[6],"manufacturing":[7],"(AM)":[8],"is":[9],"essential":[10],"for":[11,63,217],"understanding":[12],"the":[13,45,99,106,159,174,185,197,207],"process-structure-performance":[14],"relationship.":[15],"While":[16],"prior":[17],"studies":[18,190],"have":[19],"explored":[20],"generalization":[21,64],"to":[22,44,95,128,164,173,222],"unseen":[23,66],"process":[24],"conditions,":[25],"they":[26],"often":[27],"require":[28],"extensive":[29],"datasets,":[30],"costly":[31],"retraining,":[32,71],"or":[33,72],"pre-training.":[34,73],"Generalization":[35],"across":[36,65,140],"different":[37],"materials":[38,67],"also":[39],"remains":[40],"relatively":[41],"unexplored":[42],"due":[43],"challenges":[46],"posed":[47],"by":[48],"distinct":[49],"material-dependent":[50],"thermal":[51],"behaviors.":[52],"This":[53],"paper":[54],"introduces":[55],"a":[56,77,121,165],"parametric":[57,79,204],"physics-informed":[58],"neural":[59],"network":[60],"(PINN)":[61],"framework":[62,75,161,209],"without":[68],"labeled":[69],"data,":[70],"The":[74],"adopts":[76],"decoupled":[78],"PINN":[80],"architecture":[81],"that":[82],"separately":[83],"encodes":[84],"material":[85,103],"properties":[86],"and":[87,109,120,134,147,195,213,225],"spatiotemporal":[88],"coordinates,":[89],"fusing":[90],"them":[91],"through":[92],"conditional":[93],"modulation":[94],"better":[96],"align":[97],"with":[98,137,154],"multiplicative":[100],"role":[101],"of":[102,184],"parameters":[104],"governing":[107],"equation":[108],"boundary":[110],"conditions.":[111],"Physics-guided":[112],"output":[113],"scaling":[114],"derived":[115],"from":[116],"Rosenthal's":[117],"analytical":[118],"solution":[119,216],"hybrid":[122],"optimization":[123],"strategy":[124],"are":[125],"further":[126],"incorporated":[127],"enhance":[129],"physical":[130],"consistency,":[131],"training":[132,156,187,199],"stability,":[133],"convergence.":[135],"Experiments":[136],"numerical":[138],"simulations":[139],"diverse":[141],"alloys,":[143],"including":[144],"both":[145],"in-distribution":[146],"out-of-distribution":[148],"cases,":[149],"demonstrate":[150],"effective":[151],"generalizability":[152],"along":[153],"superior":[155],"efficiency.":[157],"Specifically,":[158],"proposed":[160,208],"achieved":[162],"up":[163],"64.2%":[166],"reduction":[167],"relative":[169],"L2":[170],"error":[171],"compared":[172],"non-parametric":[175],"baseline":[176,186],"while":[177],"surpassing":[178],"its":[179],"performance":[180],"within":[181],"only":[182],"4.4%":[183],"epochs.":[188],"Ablation":[189],"clarify":[191],"each":[192],"component's":[193],"contribution":[194],"scrutinize":[196],"severe":[198],"instability":[200],"prevalent":[201],"conventional":[203],"PINNs.":[205],"Overall,":[206],"provides":[210],"an":[211],"efficient":[212],"scalable":[214],"material-agnostic":[215],"modeling,":[220],"contributing":[221],"more":[223],"flexible":[224],"practical":[226],"deployment":[227],"AM.":[230]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-04-18T00:00:00"}
