{"id":"https://openalex.org/W4416252053","doi":"https://doi.org/10.1109/ijcnn64981.2025.11229005","title":"Efficient Manifold-Constrained Neural ODE for High-Dimensional Datasets","display_name":"Efficient Manifold-Constrained Neural ODE for High-Dimensional Datasets","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4416252053","doi":"https://doi.org/10.1109/ijcnn64981.2025.11229005"},"language":null,"primary_location":{"id":"doi:10.1109/ijcnn64981.2025.11229005","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11229005","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/A5076271543","display_name":"Muhao Guo","orcid":"https://orcid.org/0000-0002-9890-8214"},"institutions":[{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Muhao Guo","raw_affiliation_strings":["Arizona State University,Department of ECEE,Tempe,United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Arizona State University,Department of ECEE,Tempe,United States","institution_ids":["https://openalex.org/I55732556"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100327731","display_name":"Haoran Li","orcid":"https://orcid.org/0000-0002-5641-1058"},"institutions":[{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Haoran Li","raw_affiliation_strings":["Arizona State University,Department of ECEE,Tempe,United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Arizona State University,Department of ECEE,Tempe,United States","institution_ids":["https://openalex.org/I55732556"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5021106309","display_name":"Yang Weng","orcid":"https://orcid.org/0000-0002-5267-1303"},"institutions":[{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yang Weng","raw_affiliation_strings":["Arizona State University,Department of ECEE,Tempe,United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Arizona State University,Department of ECEE,Tempe,United States","institution_ids":["https://openalex.org/I55732556"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.23980122,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"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":0.9537000060081482,"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":0.9537000060081482,"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":0.007899999618530273,"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/T12303","display_name":"Tensor decomposition and applications","score":0.004699999932199717,"subfield":{"id":"https://openalex.org/subfields/2605","display_name":"Computational Mathematics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/ode","display_name":"Ode","score":0.873199999332428},{"id":"https://openalex.org/keywords/ordinary-differential-equation","display_name":"Ordinary differential equation","score":0.5835999846458435},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5170999765396118},{"id":"https://openalex.org/keywords/truncation","display_name":"Truncation (statistics)","score":0.45829999446868896},{"id":"https://openalex.org/keywords/projection","display_name":"Projection (relational algebra)","score":0.4147000014781952},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4032999873161316},{"id":"https://openalex.org/keywords/manifold","display_name":"Manifold (fluid mechanics)","score":0.400299996137619},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.3977999985218048}],"concepts":[{"id":"https://openalex.org/C34862557","wikidata":"https://www.wikidata.org/wiki/Q178985","display_name":"Ode","level":2,"score":0.873199999332428},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6039999723434448},{"id":"https://openalex.org/C51544822","wikidata":"https://www.wikidata.org/wiki/Q465274","display_name":"Ordinary differential equation","level":3,"score":0.5835999846458435},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5170999765396118},{"id":"https://openalex.org/C106195933","wikidata":"https://www.wikidata.org/wiki/Q7847935","display_name":"Truncation (statistics)","level":2,"score":0.45829999446868896},{"id":"https://openalex.org/C57493831","wikidata":"https://www.wikidata.org/wiki/Q3134666","display_name":"Projection (relational algebra)","level":2,"score":0.4147000014781952},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4092999994754791},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4032999873161316},{"id":"https://openalex.org/C529865628","wikidata":"https://www.wikidata.org/wiki/Q1790740","display_name":"Manifold (fluid mechanics)","level":2,"score":0.400299996137619},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.3977999985218048},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.3917999863624573},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3833000063896179},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.3540000021457672},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3212999999523163},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.31310001015663147},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3109999895095825},{"id":"https://openalex.org/C78045399","wikidata":"https://www.wikidata.org/wiki/Q11214","display_name":"Differential equation","level":2,"score":0.290800005197525},{"id":"https://openalex.org/C142730499","wikidata":"https://www.wikidata.org/wiki/Q934367","display_name":"Function space","level":2,"score":0.2867000102996826},{"id":"https://openalex.org/C153120616","wikidata":"https://www.wikidata.org/wiki/Q17068315","display_name":"Manifold alignment","level":4,"score":0.27880001068115234},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.27219998836517334},{"id":"https://openalex.org/C151876577","wikidata":"https://www.wikidata.org/wiki/Q7049464","display_name":"Nonlinear dimensionality reduction","level":3,"score":0.27090001106262207},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.2653999924659729},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.2653000056743622},{"id":"https://openalex.org/C2781170535","wikidata":"https://www.wikidata.org/wiki/Q30587856","display_name":"Noisy data","level":2,"score":0.2639999985694885},{"id":"https://openalex.org/C21080849","wikidata":"https://www.wikidata.org/wiki/Q13611879","display_name":"Data point","level":2,"score":0.2606000006198883},{"id":"https://openalex.org/C93226319","wikidata":"https://www.wikidata.org/wiki/Q193137","display_name":"Differential (mechanical device)","level":2,"score":0.25690001249313354},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.2529999911785126}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn64981.2025.11229005","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11229005","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":21,"referenced_works":["https://openalex.org/W434012021","https://openalex.org/W1521738998","https://openalex.org/W1587799944","https://openalex.org/W1976068300","https://openalex.org/W2007339694","https://openalex.org/W2125003829","https://openalex.org/W2302255633","https://openalex.org/W2963693826","https://openalex.org/W3167040424","https://openalex.org/W3197209004","https://openalex.org/W4213010207","https://openalex.org/W4224996109","https://openalex.org/W4253175466","https://openalex.org/W4313166399","https://openalex.org/W4313214554","https://openalex.org/W4367046617","https://openalex.org/W4379985192","https://openalex.org/W4385488791","https://openalex.org/W4386067122","https://openalex.org/W4387006372","https://openalex.org/W4396723398"],"related_works":[],"abstract_inverted_index":{"Neural":[0],"ordinary":[1],"differential":[2],"equations":[3],"(NODE)":[4],"have":[5],"garnered":[6],"significant":[7,143],"attention":[8],"for":[9,24,38,73],"their":[10],"design":[11],"of":[12,56,70,162,169,183,190],"continuous-depth":[13],"neural":[14],"networks":[15],"and":[16,32,119,148,166],"the":[17,39,44,52,57,71,79,83,102,107,121,126,135,139,159,181,188],"ability":[18],"to":[19,50,100,105,116,124,133],"learn":[20],"data/feature":[21],"dynamics.":[22],"However,":[23],"high-dimensional":[25,191],"systems,":[26],"estimating":[27],"dynamics":[28],"requires":[29],"extensive":[30],"calculations":[31],"suffers":[33],"from":[34],"high":[35],"truncation":[36],"errors":[37],"ODE":[40,80,108],"solvers.":[41],"To":[42],"address":[43],"issue,":[45],"one":[46],"intuitive":[47],"approach":[48,99,185],"is":[49,88],"consider":[51],"non-trivial":[53],"topological":[54],"space":[55],"data":[58,118],"distribution,":[59],"i.e.,":[60],"a":[61,97,113],"low-dimensional":[62],"manifold.":[63,84,127],"Existing":[64],"methods":[65,132],"often":[66],"rely":[67],"on":[68,82],"knowledge":[69,87],"manifold":[72,104],"projection":[74],"or":[75],"implicit":[76],"transformation,":[77],"restricting":[78],"solutions":[81],"Nevertheless,":[85],"such":[86],"usually":[89],"unknown":[90],"in":[91,142,145,186],"realistic":[92],"scenarios.":[93],"Therefore,":[94],"we":[95,111,129,157],"propose":[96,130],"novel":[98,131],"explore":[101],"underlying":[103,122],"restrict":[106],"process.":[109],"Specifically,":[110],"employ":[112],"structure-preserved":[114],"encoder":[115],"process":[117],"find":[120],"graph":[123],"approximate":[125],"Moreover,":[128],"combine":[134],"NODE":[136],"learning":[137],"with":[138],"manifold,":[140],"resulting":[141],"gains":[144],"computational":[146],"speed":[147,168],"accuracy.":[149],"Our":[150,175],"experimental":[151],"evaluations":[152,164],"encompass":[153],"multiple":[154],"datasets,":[155],"where":[156],"compare":[158],"accuracy,":[160],"number":[161],"function":[163],"(NFEs),":[165],"convergence":[167],"our":[170,184],"model":[171],"against":[172],"existing":[173],"baselines.":[174],"results":[176],"demonstrate":[177],"superior":[178],"performance,":[179],"underscoring":[180],"effectiveness":[182],"addressing":[187],"challenges":[189],"datasets.":[192]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-11-14T00:00:00"}
