{"id":"https://openalex.org/W7161016855","doi":"https://doi.org/10.1145/3744255.3811718","title":"Unsupervised Learning for AC Optimal Power Flow with Fast Physics-Aware Layer","display_name":"Unsupervised Learning for AC Optimal Power Flow with Fast Physics-Aware Layer","publication_year":2026,"publication_date":"2026-06-16","ids":{"openalex":"https://openalex.org/W7161016855","doi":"https://doi.org/10.1145/3744255.3811718"},"language":null,"primary_location":{"id":"doi:10.1145/3744255.3811718","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3744255.3811718","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 17th ACM International Conference on Future and Sustainable Energy Systems","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3744255.3811718","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5134798548","display_name":"Jiebao Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I30809798","display_name":"ShanghaiTech University","ror":"https://ror.org/030bhh786","country_code":"CN","type":"education","lineage":["https://openalex.org/I30809798"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiebao Zhang","raw_affiliation_strings":["Shanghaitech university, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0001-5065-8204","affiliations":[{"raw_affiliation_string":"Shanghaitech university, Shanghai, China","institution_ids":["https://openalex.org/I30809798"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134776949","display_name":"Haoyu Yan","orcid":null},"institutions":[{"id":"https://openalex.org/I30809798","display_name":"ShanghaiTech University","ror":"https://ror.org/030bhh786","country_code":"CN","type":"education","lineage":["https://openalex.org/I30809798"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haoyu Yan","raw_affiliation_strings":["Shanghaitech university, Shanghai, China"],"raw_orcid":"https://orcid.org/0009-0002-2745-5622","affiliations":[{"raw_affiliation_string":"Shanghaitech university, Shanghai, China","institution_ids":["https://openalex.org/I30809798"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134789130","display_name":"Zhichao Sheng","orcid":null},"institutions":[{"id":"https://openalex.org/I113940042","display_name":"Shanghai University","ror":"https://ror.org/006teas31","country_code":"CN","type":"education","lineage":["https://openalex.org/I113940042"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhichao Sheng","raw_affiliation_strings":["Shanghai University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0002-2636-5214","affiliations":[{"raw_affiliation_string":"Shanghai University, Shanghai, China","institution_ids":["https://openalex.org/I113940042"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100742422","display_name":"Hongwen Yu","orcid":"https://orcid.org/0000-0003-2631-4764"},"institutions":[{"id":"https://openalex.org/I113940042","display_name":"Shanghai University","ror":"https://ror.org/006teas31","country_code":"CN","type":"education","lineage":["https://openalex.org/I113940042"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongwen Yu","raw_affiliation_strings":["Shanghai University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0003-2631-4764","affiliations":[{"raw_affiliation_string":"Shanghai University, Shanghai, China","institution_ids":["https://openalex.org/I113940042"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101668492","display_name":"Shuang Ye","orcid":"https://orcid.org/0000-0002-0238-3194"},"institutions":[{"id":"https://openalex.org/I30809798","display_name":"ShanghaiTech University","ror":"https://ror.org/030bhh786","country_code":"CN","type":"education","lineage":["https://openalex.org/I30809798"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuang Ye","raw_affiliation_strings":["ShanghaiTech University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0002-0238-3194","affiliations":[{"raw_affiliation_string":"ShanghaiTech University, Shanghai, China","institution_ids":["https://openalex.org/I30809798"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136031919","display_name":"Haoyu Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I30809798","display_name":"ShanghaiTech University","ror":"https://ror.org/030bhh786","country_code":"CN","type":"education","lineage":["https://openalex.org/I30809798"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haoyu Wang","raw_affiliation_strings":["Shanghaitech university, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0002-2124-3453","affiliations":[{"raw_affiliation_string":"Shanghaitech university, Shanghai, China","institution_ids":["https://openalex.org/I30809798"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5136052687","display_name":"Ye Shi","orcid":null},"institutions":[{"id":"https://openalex.org/I30809798","display_name":"ShanghaiTech University","ror":"https://ror.org/030bhh786","country_code":"CN","type":"education","lineage":["https://openalex.org/I30809798"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ye Shi","raw_affiliation_strings":["Shanghaitech university, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0003-2302-4980","affiliations":[{"raw_affiliation_string":"Shanghaitech university, Shanghai, China","institution_ids":["https://openalex.org/I30809798"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.56182242,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"504","last_page":"520"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10454","display_name":"Optimal Power Flow Distribution","score":0.7870000004768372,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/T10454","display_name":"Optimal Power Flow Distribution","score":0.7870000004768372,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/T10305","display_name":"Power System Optimization and Stability","score":0.05429999902844429,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/T11206","display_name":"Model Reduction and Neural Networks","score":0.03750000149011612,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/solver","display_name":"Solver","score":0.7175999879837036},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.5483999848365784},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5467000007629395},{"id":"https://openalex.org/keywords/constraint","display_name":"Constraint (computer-aided design)","score":0.48899999260902405},{"id":"https://openalex.org/keywords/jacobian-matrix-and-determinant","display_name":"Jacobian matrix and determinant","score":0.47929999232292175},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.4553999900817871},{"id":"https://openalex.org/keywords/flow","display_name":"Flow (mathematics)","score":0.40779998898506165},{"id":"https://openalex.org/keywords/power","display_name":"Power (physics)","score":0.40709999203681946},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.39259999990463257}],"concepts":[{"id":"https://openalex.org/C2778770139","wikidata":"https://www.wikidata.org/wiki/Q1966904","display_name":"Solver","level":2,"score":0.7175999879837036},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7013000249862671},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.5483999848365784},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5467000007629395},{"id":"https://openalex.org/C2776036281","wikidata":"https://www.wikidata.org/wiki/Q48769818","display_name":"Constraint (computer-aided design)","level":2,"score":0.48899999260902405},{"id":"https://openalex.org/C200331156","wikidata":"https://www.wikidata.org/wiki/Q506041","display_name":"Jacobian matrix and determinant","level":2,"score":0.47929999232292175},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.4553999900817871},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.41290000081062317},{"id":"https://openalex.org/C38349280","wikidata":"https://www.wikidata.org/wiki/Q1434290","display_name":"Flow (mathematics)","level":2,"score":0.40779998898506165},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.40709999203681946},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.39259999990463257},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.39160001277923584},{"id":"https://openalex.org/C167879884","wikidata":"https://www.wikidata.org/wiki/Q727568","display_name":"Balanced flow","level":2,"score":0.3864000141620636},{"id":"https://openalex.org/C120822770","wikidata":"https://www.wikidata.org/wiki/Q5156355","display_name":"Competitive learning","level":3,"score":0.3849000036716461},{"id":"https://openalex.org/C2779227376","wikidata":"https://www.wikidata.org/wiki/Q6505497","display_name":"Layer (electronics)","level":2,"score":0.3716000020503998},{"id":"https://openalex.org/C159694833","wikidata":"https://www.wikidata.org/wiki/Q2321565","display_name":"Iterative method","level":2,"score":0.36629998683929443},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.35019999742507935},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.3447999954223633},{"id":"https://openalex.org/C2986056383","wikidata":"https://www.wikidata.org/wiki/Q556030","display_name":"Power flow","level":4,"score":0.32829999923706055},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.271699994802475},{"id":"https://openalex.org/C133512626","wikidata":"https://www.wikidata.org/wiki/Q787371","display_name":"Automatic differentiation","level":3,"score":0.2624000012874603},{"id":"https://openalex.org/C116672817","wikidata":"https://www.wikidata.org/wiki/Q1454986","display_name":"Physical system","level":2,"score":0.26019999384880066},{"id":"https://openalex.org/C153258448","wikidata":"https://www.wikidata.org/wiki/Q1199743","display_name":"Gradient descent","level":3,"score":0.25049999356269836},{"id":"https://openalex.org/C117619785","wikidata":"https://www.wikidata.org/wiki/Q6094414","display_name":"Iterative learning control","level":3,"score":0.2500999867916107}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3744255.3811718","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3744255.3811718","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 17th ACM International Conference on Future and Sustainable Energy Systems","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2604.23548","is_oa":true,"landing_page_url":"https://arxiv.org/abs/2604.23548","pdf_url":"https://arxiv.org/pdf/2604.23548","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"doi:10.1145/3744255.3811718","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3744255.3811718","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 17th ACM International Conference on Future and Sustainable Energy Systems","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Learning":[0],"to":[1,24],"solve":[2],"the":[3,26,41,44,55,102,107,112,116,127,142,153],"Alternating":[4],"Current":[5],"Optimal":[6],"Power":[7],"Flow":[8],"(AC-OPF)":[9],"problem":[10],"by":[11],"neural":[12],"networks":[13],"(NNs)":[14],"is":[15,61,183],"a":[16,33,80,87,96,149],"promising":[17],"approach":[18],"in":[19],"real-time":[20],"applications.":[21],"Existing":[22],"methods":[23],"ensure":[25],"physical":[27],"feasibility":[28],"of":[29,57,152],"NN":[30,103],"outputs":[31],"embed":[32],"power":[34],"flow":[35],"(PF)":[36],"solver":[37,100],"within":[38,101],"networks.":[39],"However,":[40],"gradient":[42,143,156],"through":[43],"PF":[45,98],"solver,":[46],"namely,":[47],"implicit":[48,155],"differentiation,":[49],"needs":[50],"manual":[51],"Jacobian":[52],"derivation":[53],"and":[54,64,104,129,178],"solution":[56],"linear":[58],"systems,":[59],"which":[60],"computationally":[62],"prohibitive":[63],"hinders":[65],"integration":[66],"with":[67],"modern":[68],"automatic":[69],"differentiation":[70],"(AD)":[71],"frameworks.":[72],"To":[73],"address":[74],"these":[75],"challenges,":[76],"we":[77,138],"propose":[78],"FPL-OPF,":[79],"novel":[81],"unsupervised":[82,170],"learning":[83,171],"framework":[84],"that":[85,141,163],"incorporates":[86],"Fast":[88],"Physics-aware":[89],"Layer":[90],"for":[91,125],"AC-OPF":[92],"problems.":[93],"FPL-OPF":[94,164],"embeds":[95],"fast":[97],"iterative":[99],"takes":[105],"solely":[106],"last":[108],"few":[109],"or":[110],"even":[111],"final":[113],"iterations":[114],"into":[115],"AD":[117],"graph.":[118],"This":[119],"design":[120,146],"ensures":[121],"high":[122],"computational":[123],"efficiency":[124],"both":[126],"forward":[128],"backward":[130,135],"passes,":[131],"circumventing":[132],"complex":[133],"custom":[134],"implementations.":[136],"Theoretically,":[137],"rigorously":[139],"prove":[140],"from":[144],"this":[145],"serves":[147],"as":[148],"high-fidelity":[150],"surrogate":[151],"true":[154],"under":[157],"mild":[158],"conditions.":[159],"Extensive":[160],"experiments":[161],"demonstrate":[162],"achieves":[165],"significant":[166],"speedups":[167],"over":[168],"state-of-the-art":[169],"approaches,":[172],"while":[173],"maintaining":[174],"near-zero":[175],"constraint":[176],"violations":[177],"competitive":[179],"optimality.":[180],"Our":[181],"code":[182],"available":[184],"at":[185],"https://github.com/wowotou1998/fpl-opf":[186]},"counts_by_year":[],"updated_date":"2026-07-03T08:13:44.112507","created_date":"2026-05-14T00:00:00"}