{"id":"https://openalex.org/W7164553247","doi":"https://doi.org/10.48550/arxiv.2606.13443","title":"How Much Memory Do We Need? Adaptive Memory Gate for Neural Operators","display_name":"How Much Memory Do We Need? Adaptive Memory Gate for Neural Operators","publication_year":2026,"publication_date":"2026-06-11","ids":{"openalex":"https://openalex.org/W7164553247","doi":"https://doi.org/10.48550/arxiv.2606.13443"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.13443","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.13443","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":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.13443","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5138512735","display_name":"Jihyeon Hur","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hur, Jihyeon","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5091553091","display_name":"Y. Kwon","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kwon, Yongseok","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138527960","display_name":"Min-Gi Jo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jo, Min-Gi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113468057","display_name":"J H Choi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Choi, Jeongwhan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5138524947","display_name":"Noseong Park","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Park, Noseong","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/T11206","display_name":"Model Reduction and Neural Networks","score":0.9557999968528748,"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":0.9557999968528748,"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/T12611","display_name":"Neural Networks and Reservoir Computing","score":0.012000000104308128,"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"}},{"id":"https://openalex.org/T11948","display_name":"Machine Learning in Materials Science","score":0.006300000008195639,"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/limiting","display_name":"Limiting","score":0.6086000204086304},{"id":"https://openalex.org/keywords/reduction","display_name":"Reduction (mathematics)","score":0.46459999680519104},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4431000053882599},{"id":"https://openalex.org/keywords/resolution","display_name":"Resolution (logic)","score":0.4341000020503998},{"id":"https://openalex.org/keywords/encoding","display_name":"Encoding (memory)","score":0.4146000146865845},{"id":"https://openalex.org/keywords/adaptive-memory","display_name":"Adaptive memory","score":0.4052000045776367}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6431000232696533},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.6086000204086304},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.46459999680519104},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4431000053882599},{"id":"https://openalex.org/C138268822","wikidata":"https://www.wikidata.org/wiki/Q1051925","display_name":"Resolution (logic)","level":2,"score":0.4341000020503998},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.4146000146865845},{"id":"https://openalex.org/C30390489","wikidata":"https://www.wikidata.org/wiki/Q4680748","display_name":"Adaptive memory","level":3,"score":0.4052000045776367},{"id":"https://openalex.org/C2776291640","wikidata":"https://www.wikidata.org/wiki/Q2912517","display_name":"Value (mathematics)","level":2,"score":0.3833000063896179},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.37299999594688416},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3353999853134155},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.3179999887943268},{"id":"https://openalex.org/C176649486","wikidata":"https://www.wikidata.org/wiki/Q2308807","display_name":"Memory management","level":3,"score":0.30410000681877136},{"id":"https://openalex.org/C64142963","wikidata":"https://www.wikidata.org/wiki/Q1153902","display_name":"Phase-change memory","level":3,"score":0.2896000146865845},{"id":"https://openalex.org/C94375191","wikidata":"https://www.wikidata.org/wiki/Q11205","display_name":"Arithmetic","level":1,"score":0.27090001106262207}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.13443","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.13443","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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.13443","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.13443","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":"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":{"Neural":[0],"operators":[1,18],"have":[2,24],"emerged":[3],"as":[4,45,119],"a":[5,36,68,88],"powerful":[6],"data-driven":[7],"approach":[8],"for":[9],"solving":[10],"time-dependent":[11],"PDEs.":[12],"Among":[13],"recent":[14],"advances,":[15],"memory-augmented":[16],"neural":[17],"explicitly":[19],"incorporate":[20],"past":[21],"states":[22],"and":[23,64,94],"achieved":[25],"remarkable":[26],"performance":[27,75],"under":[28],"low-resolution":[29],"observation":[30,42],"settings.":[31,78],"However,":[32],"existing":[33],"approaches":[34],"apply":[35],"fixed":[37,69],"memory":[38,59,70,85],"weight":[39,60,71,86],"regardless":[40],"of":[41],"conditions,":[43],"such":[44],"resolution":[46,63,120],"or":[47],"physical":[48],"parameters,":[49],"limiting":[50],"their":[51],"adaptability.":[52],"Our":[53],"preliminary":[54],"experiments":[55],"reveal":[56],"that":[57,67],"optimal":[58],"varies":[61],"with":[62,106],"viscosity,":[65],"implying":[66],"cannot":[72],"simultaneously":[73],"optimize":[74],"across":[76],"diverse":[77],"We":[79],"propose":[80],"AMGFNO,":[81],"which":[82],"dynamically":[83],"modulates":[84],"through":[87],"learnable":[89],"gate.":[90],"On":[91],"the":[92,107],"Kuramoto-Sivashinsky":[93],"Burgers'":[95],"equations,":[96],"AMGFNO":[97],"achieves":[98],"55-79%":[99],"nRMSE":[100],"reduction":[101],"over":[102],"at":[103],"low":[104],"resolution,":[105],"learned":[108],"gate":[109],"value":[110],"automatically":[111],"decreasing":[112],"from":[113],"$\\bar{g}":[114],"\\approx":[115],"0.7$":[116],"to":[117],"near-zero":[118],"increases.":[121]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-13T00:00:00"}
