{"id":"https://openalex.org/W4416251419","doi":"https://doi.org/10.1109/ijcnn64981.2025.11228927","title":"Integrating Locality-Aware Attention with Transformers for General Geometry PDEs","display_name":"Integrating Locality-Aware Attention with Transformers for General Geometry PDEs","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4416251419","doi":"https://doi.org/10.1109/ijcnn64981.2025.11228927"},"language":null,"primary_location":{"id":"doi:10.1109/ijcnn64981.2025.11228927","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11228927","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"article","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/A5110181642","display_name":"M. Koh","orcid":null},"institutions":[{"id":"https://openalex.org/I197347611","display_name":"Korea University","ror":"https://ror.org/047dqcg40","country_code":"KR","type":"education","lineage":["https://openalex.org/I197347611"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Minsu Koh","raw_affiliation_strings":["Korea University,Dept. of Artificial Intelligence,Seoul,South Korea"],"affiliations":[{"raw_affiliation_string":"Korea University,Dept. of Artificial Intelligence,Seoul,South Korea","institution_ids":["https://openalex.org/I197347611"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054324822","display_name":"B. Doug Park","orcid":null},"institutions":[{"id":"https://openalex.org/I197347611","display_name":"Korea University","ror":"https://ror.org/047dqcg40","country_code":"KR","type":"education","lineage":["https://openalex.org/I197347611"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Beom-Chul Park","raw_affiliation_strings":["Korea University,Dept. of Artificial Intelligence,Seoul,South Korea"],"affiliations":[{"raw_affiliation_string":"Korea University,Dept. of Artificial Intelligence,Seoul,South Korea","institution_ids":["https://openalex.org/I197347611"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113164590","display_name":"Heejo Kong","orcid":null},"institutions":[{"id":"https://openalex.org/I4210104429","display_name":"Institute of Cognitive and Brain Sciences","ror":"https://ror.org/01c3w3270","country_code":"US","type":"education","lineage":["https://openalex.org/I4210104429"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Heejo Kong","raw_affiliation_strings":["Korea University,Dept. of Brain and Cognitive Engineering,Seoul,South Korea"],"affiliations":[{"raw_affiliation_string":"Korea University,Dept. of Brain and Cognitive Engineering,Seoul,South Korea","institution_ids":["https://openalex.org/I4210104429"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5109186412","display_name":"Seong-Whan Lee","orcid":null},"institutions":[{"id":"https://openalex.org/I197347611","display_name":"Korea University","ror":"https://ror.org/047dqcg40","country_code":"KR","type":"education","lineage":["https://openalex.org/I197347611"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Seong-Whan Lee","raw_affiliation_strings":["Korea University,Dept. of Artificial Intelligence,Seoul,South Korea"],"affiliations":[{"raw_affiliation_string":"Korea University,Dept. of Artificial Intelligence,Seoul,South Korea","institution_ids":["https://openalex.org/I197347611"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5110181642"],"corresponding_institution_ids":["https://openalex.org/I197347611"],"apc_list":null,"apc_paid":null,"fwci":1.3615,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.8602514,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11206","display_name":"Model Reduction and Neural Networks","score":0.9704999923706055,"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":0.9704999923706055,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.004100000020116568,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10719","display_name":"3D Shape Modeling and Analysis","score":0.002899999963119626,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"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/pairwise-comparison","display_name":"Pairwise comparison","score":0.5217999815940857},{"id":"https://openalex.org/keywords/partial-differential-equation","display_name":"Partial differential equation","score":0.4805000126361847},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4650999903678894},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.4223000109195709},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.40700000524520874},{"id":"https://openalex.org/keywords/operator","display_name":"Operator (biology)","score":0.40450000762939453}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6110000014305115},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.5217999815940857},{"id":"https://openalex.org/C93779851","wikidata":"https://www.wikidata.org/wiki/Q271977","display_name":"Partial differential equation","level":2,"score":0.4805000126361847},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4650999903678894},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.43290001153945923},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4223000109195709},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.40700000524520874},{"id":"https://openalex.org/C17020691","wikidata":"https://www.wikidata.org/wiki/Q139677","display_name":"Operator (biology)","level":5,"score":0.40450000762939453},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.3815999925136566},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.3675000071525574},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.36719998717308044},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33169999718666077},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.321399986743927},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.31049999594688416},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.304500013589859},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2784000039100647},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.26579999923706055},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.25429999828338623}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn64981.2025.11228927","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11228927","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W2031825886","https://openalex.org/W2083677758","https://openalex.org/W2498216762","https://openalex.org/W2811105005","https://openalex.org/W2899283552","https://openalex.org/W2979786244","https://openalex.org/W3007994488","https://openalex.org/W3138516171","https://openalex.org/W3197308578","https://openalex.org/W4392946869"],"related_works":[],"abstract_inverted_index":{"Neural":[0],"operators":[1,54,188],"have":[2,33,59],"emerged":[3],"as":[4,17,27],"promising":[5],"frameworks":[6],"for":[7,66,87,105,112,120,128,189],"learning":[8,183],"mappings":[9],"governed":[10],"by":[11,156],"partial":[12],"differential":[13],"equations":[14],"(PDEs),":[15],"serving":[16],"data-driven":[18],"alternatives":[19],"to":[20,45,160],"traditional":[21],"numerical":[22],"methods.":[23],"While":[24],"methods":[25],"such":[26],"the":[28,96,177],"Fourier":[29],"neural":[30,53,187],"operator":[31],"(FNO)":[32],"demonstrated":[34],"notable":[35],"performance,":[36],"their":[37,43],"reliance":[38],"on":[39,192],"uniform":[40],"grids":[41],"restricts":[42],"applicability":[44],"complex":[46,193],"geometries":[47],"and":[48,82,108,141,194],"irregular":[49,195],"meshes.":[50],"Recently,":[51],"Transformer-based":[52,186],"with":[55,125],"linear":[56,118,162],"attention":[57,111,119,127,163,170],"mechanisms":[58],"shown":[60],"potential":[61],"in":[62,184],"overcoming":[63],"these":[64,71,92],"limitations":[65],"large-scale":[67],"PDE":[68,84,114],"simulations.":[69],"However,":[70],"approaches":[72],"predominantly":[73],"emphasize":[74],"global":[75,122],"feature":[76,182],"aggregation,":[77],"often":[78],"overlooking":[79],"fine-scale":[80],"dynamics":[81],"localized":[83,181],"behaviors":[85],"essential":[86],"accurate":[88],"solutions.":[89],"To":[90],"address":[91],"challenges,":[93],"we":[94],"propose":[95],"Locality-Aware":[97],"Attention":[98],"Transformer":[99],"(LA2Former),":[100],"which":[101],"leverages":[102],"K-nearest":[103],"neighbors":[104],"dynamic":[106],"patchifying":[107],"integrates":[109],"global-local":[110],"enhanced":[113],"modeling.":[115],"By":[116],"combining":[117],"efficient":[121],"context":[123],"encoding":[124],"pairwise":[126,169],"capturing":[129],"intricate":[130],"local":[131],"interactions,":[132],"LA2Former":[133,152],"achieves":[134],"an":[135],"optimal":[136,172],"balance":[137],"between":[138],"computational":[139],"efficiency":[140],"predictive":[142,154],"accuracy.":[143],"Extensive":[144],"evaluations":[145],"across":[146],"six":[147],"benchmark":[148],"datasets":[149],"demonstrate":[150],"that":[151],"improves":[153],"accuracy":[155],"over":[157],"50%":[158],"relative":[159],"existing":[161],"methods,":[164],"while":[165],"also":[166],"outperforming":[167],"full":[168],"under":[171],"conditions.":[173],"This":[174],"work":[175],"underscores":[176],"critical":[178],"importance":[179],"of":[180],"advancing":[185],"solving":[190],"PDEs":[191],"domains.":[196],"Code":[197],"is":[198],"available":[199],"at":[200],"https://github.com/komingsu/LA2Former.":[201]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-03-24T08:02:53.985720","created_date":"2025-11-14T00:00:00"}