{"id":"https://openalex.org/W7138891606","doi":"https://doi.org/10.1609/aaai.v40i43.41003","title":"Exploring Non-Convex Discrete Energy Landscapes: An Efficient Langevin-Like Sampler with Replica Exchange","display_name":"Exploring Non-Convex Discrete Energy Landscapes: An Efficient Langevin-Like Sampler with Replica Exchange","publication_year":2026,"publication_date":"2026-03-14","ids":{"openalex":"https://openalex.org/W7138891606","doi":"https://doi.org/10.1609/aaai.v40i43.41003"},"language":"en","primary_location":{"id":"doi:10.1609/aaai.v40i43.41003","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i43.41003","pdf_url":"https://ojs.aaai.org/index.php/AAAI/article/download/41003/44964","source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://ojs.aaai.org/index.php/AAAI/article/download/41003/44964","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5129783490","display_name":"Haoyang Zheng","orcid":null},"institutions":[{"id":"https://openalex.org/I219193219","display_name":"Purdue University West Lafayette","ror":"https://ror.org/02dqehb95","country_code":"US","type":"education","lineage":["https://openalex.org/I219193219"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Haoyang Zheng","raw_affiliation_strings":["Purdue University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Purdue University","institution_ids":["https://openalex.org/I219193219"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072246740","display_name":"Hengrong Du","orcid":"https://orcid.org/0000-0003-2392-8963"},"institutions":[{"id":"https://openalex.org/I9254433","display_name":"Fisk University","ror":"https://ror.org/01bbs6821","country_code":"US","type":"education","lineage":["https://openalex.org/I9254433"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hengrong Du","raw_affiliation_strings":["Fisk University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fisk University","institution_ids":["https://openalex.org/I9254433"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130039513","display_name":"Ruqi Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I219193219","display_name":"Purdue University West Lafayette","ror":"https://ror.org/02dqehb95","country_code":"US","type":"education","lineage":["https://openalex.org/I219193219"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ruqi Zhang","raw_affiliation_strings":["Purdue University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Purdue University","institution_ids":["https://openalex.org/I219193219"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5130102787","display_name":"Guang Lin","orcid":null},"institutions":[{"id":"https://openalex.org/I219193219","display_name":"Purdue University West Lafayette","ror":"https://ror.org/02dqehb95","country_code":"US","type":"education","lineage":["https://openalex.org/I219193219"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Guang Lin","raw_affiliation_strings":["Purdue University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Purdue University","institution_ids":["https://openalex.org/I219193219"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"40","issue":"43","first_page":"36775","last_page":"36783"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.36480000615119934,"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"}},"topics":[{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.36480000615119934,"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/T12056","display_name":"Markov Chains and Monte Carlo Methods","score":0.15469999611377716,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11804","display_name":"Quantum many-body systems","score":0.06830000132322311,"subfield":{"id":"https://openalex.org/subfields/3107","display_name":"Atomic and Molecular Physics, and Optics"},"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/replica","display_name":"Replica","score":0.7630000114440918},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.7384999990463257},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.5633999705314636},{"id":"https://openalex.org/keywords/energy","display_name":"Energy (signal processing)","score":0.5426999926567078},{"id":"https://openalex.org/keywords/distribution","display_name":"Distribution (mathematics)","score":0.4077000021934509},{"id":"https://openalex.org/keywords/discrete-time-and-continuous-time","display_name":"Discrete time and continuous time","score":0.4052000045776367},{"id":"https://openalex.org/keywords/efficient-energy-use","display_name":"Efficient energy use","score":0.39419999718666077},{"id":"https://openalex.org/keywords/energy-balance","display_name":"Energy balance","score":0.376800000667572},{"id":"https://openalex.org/keywords/ising-model","display_name":"Ising model","score":0.3765000104904175}],"concepts":[{"id":"https://openalex.org/C2775937380","wikidata":"https://www.wikidata.org/wiki/Q1232589","display_name":"Replica","level":2,"score":0.7630000114440918},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.7384999990463257},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.5633999705314636},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.5426999926567078},{"id":"https://openalex.org/C121864883","wikidata":"https://www.wikidata.org/wiki/Q677916","display_name":"Statistical physics","level":1,"score":0.4740999937057495},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.46950000524520874},{"id":"https://openalex.org/C110121322","wikidata":"https://www.wikidata.org/wiki/Q865811","display_name":"Distribution (mathematics)","level":2,"score":0.4077000021934509},{"id":"https://openalex.org/C55689738","wikidata":"https://www.wikidata.org/wiki/Q15963867","display_name":"Discrete time and continuous time","level":2,"score":0.4052000045776367},{"id":"https://openalex.org/C2742236","wikidata":"https://www.wikidata.org/wiki/Q924713","display_name":"Efficient energy use","level":2,"score":0.39419999718666077},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.38580000400543213},{"id":"https://openalex.org/C2777423268","wikidata":"https://www.wikidata.org/wiki/Q3663116","display_name":"Energy balance","level":2,"score":0.376800000667572},{"id":"https://openalex.org/C51329190","wikidata":"https://www.wikidata.org/wiki/Q1076349","display_name":"Ising model","level":2,"score":0.3765000104904175},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.35920000076293945},{"id":"https://openalex.org/C135694725","wikidata":"https://www.wikidata.org/wiki/Q1201087","display_name":"Detailed balance","level":2,"score":0.35600000619888306},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.34209999442100525},{"id":"https://openalex.org/C52740198","wikidata":"https://www.wikidata.org/wiki/Q1539564","display_name":"Importance sampling","level":3,"score":0.3352999985218048},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.32199999690055847},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.32109999656677246},{"id":"https://openalex.org/C128747166","wikidata":"https://www.wikidata.org/wiki/Q7881414","display_name":"Umbrella sampling","level":3,"score":0.31859999895095825},{"id":"https://openalex.org/C35304006","wikidata":"https://www.wikidata.org/wiki/Q5962","display_name":"Boltzmann constant","level":2,"score":0.3009999990463257},{"id":"https://openalex.org/C147203929","wikidata":"https://www.wikidata.org/wiki/Q574814","display_name":"Discrete event simulation","level":2,"score":0.30079999566078186},{"id":"https://openalex.org/C190669063","wikidata":"https://www.wikidata.org/wiki/Q5282043","display_name":"Discrete choice","level":2,"score":0.2985000014305115},{"id":"https://openalex.org/C170593435","wikidata":"https://www.wikidata.org/wiki/Q4128565","display_name":"Slice sampling","level":4,"score":0.29409998655319214},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.29159998893737793},{"id":"https://openalex.org/C167723999","wikidata":"https://www.wikidata.org/wiki/Q3773214","display_name":"Sampling distribution","level":2,"score":0.28040000796318054},{"id":"https://openalex.org/C193999330","wikidata":"https://www.wikidata.org/wiki/Q834200","display_name":"Boltzmann distribution","level":2,"score":0.28029999136924744},{"id":"https://openalex.org/C75373757","wikidata":"https://www.wikidata.org/wiki/Q7410160","display_name":"Sampling design","level":3,"score":0.27639999985694885},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2639999985694885},{"id":"https://openalex.org/C2986766689","wikidata":"https://www.wikidata.org/wiki/Q676081","display_name":"Energy exchange","level":2,"score":0.2624000012874603}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1609/aaai.v40i43.41003","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i43.41003","pdf_url":"https://ojs.aaai.org/index.php/AAAI/article/download/41003/44964","source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},{"id":"pmh:oai:ojs.aaai.org:article/41003","is_oa":false,"landing_page_url":"https://ojs.aaai.org/index.php/AAAI/article/view/41003","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"2159-5399","raw_type":"info:eu-repo/semantics/article"}],"best_oa_location":{"id":"doi:10.1609/aaai.v40i43.41003","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i43.41003","pdf_url":"https://ojs.aaai.org/index.php/AAAI/article/download/41003/44964","source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Affordable and clean energy","score":0.8824830055236816,"id":"https://metadata.un.org/sdg/7"}],"awards":[{"id":"https://openalex.org/G2023003359","display_name":null,"funder_award_id":"CBET-2347401","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G214416105","display_name":null,"funder_award_id":"DE-SC0024583","funder_id":"https://openalex.org/F4320337510","funder_display_name":"Fusion Energy Sciences"},{"id":"https://openalex.org/G2528441397","display_name":null,"funder_award_id":"OAC-2311848","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G2778948316","display_name":null,"funder_award_id":"ECCS-2328241","funder_id":"https://openalex.org/F4320337392","funder_display_name":"Division of Electrical, Communications and Cyber Systems"},{"id":"https://openalex.org/G3120326796","display_name":"RI: Small: Gradient-Based Discrete Markov Chain Monte Carlo: Algorithms, Theoretical Foundations, and Applications","funder_award_id":"2508145","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G3522074516","display_name":null,"funder_award_id":"DE-SC0023161","funder_id":"https://openalex.org/F4320306084","funder_display_name":"U.S. Department of Energy"},{"id":"https://openalex.org/G3603044803","display_name":null,"funder_award_id":"DMS-2053746","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G3723704744","display_name":null,"funder_award_id":"CBET-2347401","funder_id":"https://openalex.org/F4320337390","funder_display_name":"Division of Chemical, Bioengineering, Environmental, and Transport Systems"},{"id":"https://openalex.org/G3761438914","display_name":null,"funder_award_id":"DE-SC0023161","funder_id":"https://openalex.org/F4320332359","funder_display_name":"Office of Science"},{"id":"https://openalex.org/G4275346382","display_name":"Collaborative Research: Robust Deep Learning in Real Physical Space: Generalization, Scalability, and Credibility","funder_award_id":"2134209","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G446273302","display_name":"Collaborative Research: Inference and Uncertainty Quantification for High Dimensional Systems in Remote Sensing: Methods, Computation, and Applications","funder_award_id":"2053746","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G4655918302","display_name":null,"funder_award_id":"DMS-2134209","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G5412551815","display_name":null,"funder_award_id":"DE-SC0024583","funder_id":"https://openalex.org/F4320306084","funder_display_name":"U.S. Department of Energy"},{"id":"https://openalex.org/G6355769530","display_name":null,"funder_award_id":"DE-SC0023161","funder_id":"https://openalex.org/F4320337506","funder_display_name":"Advanced Scientific Computing Research"},{"id":"https://openalex.org/G6480653435","display_name":null,"funder_award_id":"ECCS-2328241","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G7109742449","display_name":"ASCENT: From sensors to multiscale digital twin to autonomous operation of resilient electric power grids","funder_award_id":"2328241","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G7405926644","display_name":"Elements: FourPhonon: A Computational Tool for Higher-Order Phonon  Anharmonicity and Thermal Properties","funder_award_id":"2311848","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G8215743410","display_name":"CDS&E: Integrated Molecular Dynamics and Machine Learning Investigation of Gas-Liquid Interface in Transcritical Reacting Flows","funder_award_id":"2347401","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320306084","display_name":"U.S. Department of Energy","ror":"https://ror.org/01bj3aw27"},{"id":"https://openalex.org/F4320332359","display_name":"Office of Science","ror":"https://ror.org/00mmn6b08"},{"id":"https://openalex.org/F4320337390","display_name":"Division of Chemical, Bioengineering, Environmental, and Transport Systems","ror":"https://ror.org/0471zv972"},{"id":"https://openalex.org/F4320337392","display_name":"Division of Electrical, Communications and Cyber Systems","ror":"https://ror.org/01krpsy48"},{"id":"https://openalex.org/F4320337506","display_name":"Advanced Scientific Computing Research","ror":"https://ror.org/0012c7r22"},{"id":"https://openalex.org/F4320337510","display_name":"Fusion Energy Sciences","ror":"https://ror.org/02n3j8t12"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7138891606.pdf","grobid_xml":"https://content.openalex.org/works/W7138891606.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Gradient-based":[0],"Discrete":[1,25],"Samplers":[2],"(GDSs)":[3],"are":[4,64],"effective":[5],"for":[6,74],"sampling":[7,76,105],"discrete":[8,75,125],"energy":[9,59,62,126],"landscapes.":[10,60,127],"However,":[11],"they":[12],"often":[13],"stagnate":[14],"in":[15,122],"complex,":[16],"non-convex":[17,124],"settings.":[18],"To":[19],"improve":[20],"exploration,":[21],"we":[22,82],"introduce":[23],"the":[24,55,85,94],"Replica":[26],"EXchangE":[27],"Langevin":[28],"(DREXEL)":[29],"sampler":[30],"and":[31,46,91,109,113],"its":[32],"variant":[33],"with":[34],"Adjusted":[35],"Metropolis":[36],"(DREAM).":[37],"These":[38],"samplers":[39,87],"use":[40],"two":[41],"GDSs":[42],"at":[43],"different":[44],"temperatures":[45],"step":[47],"sizes:":[48],"one":[49],"focuses":[50],"on":[51],"local":[52],"exploitation,":[53],"while":[54],"other":[56],"explores":[57],"broader":[58],"When":[61],"differences":[63],"significant,":[65],"sample":[66],"swaps":[67],"occur,":[68],"governed":[69],"by":[70],"a":[71],"mechanism":[72],"tailored":[73],"to":[77,93],"ensure":[78],"detailed":[79,89],"balance.":[80],"Theoretically,":[81],"prove":[83],"that":[84],"proposed":[86],"satisfy":[88],"balance":[90],"converge":[92],"target":[95],"distribution":[96],"under":[97],"mild":[98],"conditions.":[99],"Experiments":[100],"across":[101],"2d":[102],"synthetic":[103],"simulations,":[104],"from":[106],"Ising":[107],"models":[108,117],"restricted":[110],"Boltzmann":[111],"machines,":[112],"training":[114],"deep":[115],"energy-based":[116],"further":[118],"confirm":[119],"their":[120],"efficiency":[121],"exploring":[123]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2026-03-20T00:00:00"}