{"id":"https://openalex.org/W7160720491","doi":"https://doi.org/10.1016/j.neunet.2026.109031","title":"Reservoir computing for system identification and model predictive control","display_name":"Reservoir computing for system identification and model predictive control","publication_year":2026,"publication_date":"2026-05-09","ids":{"openalex":"https://openalex.org/W7160720491","doi":"https://doi.org/10.1016/j.neunet.2026.109031","pmid":"https://pubmed.ncbi.nlm.nih.gov/42143957"},"language":"en","primary_location":{"id":"doi:10.1016/j.neunet.2026.109031","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.neunet.2026.109031","pdf_url":null,"source":{"id":"https://openalex.org/S123019304","display_name":"Neural Networks","issn_l":"0893-6080","issn":["0893-6080","1879-2782"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Neural Networks","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://doi.org/10.1016/j.neunet.2026.109031","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5088566148","display_name":"Jan P. Williams","orcid":"https://orcid.org/0009-0005-4955-0411"},"institutions":[{"id":"https://openalex.org/I201448701","display_name":"University of Washington","ror":"https://ror.org/00cvxb145","country_code":"US","type":"education","lineage":["https://openalex.org/I201448701"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Jan P. Williams","raw_affiliation_strings":["Department of Mechanical Engineering, University of Washington, Seattle, Washington, 98195, USA. Electronic address: jmpw1@uw.edu"],"raw_orcid":"https://orcid.org/0009-0005-4955-0411","affiliations":[{"raw_affiliation_string":"Department of Mechanical Engineering, University of Washington, Seattle, Washington, 98195, USA. Electronic address: jmpw1@uw.edu","institution_ids":["https://openalex.org/I201448701"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135746503","display_name":"J. Nathan Kutz","orcid":null},"institutions":[{"id":"https://openalex.org/I201448701","display_name":"University of Washington","ror":"https://ror.org/00cvxb145","country_code":"US","type":"education","lineage":["https://openalex.org/I201448701"]},{"id":"https://openalex.org/I4210138199","display_name":"University of Washington Applied Physics Laboratory","ror":"https://ror.org/03d17d270","country_code":"US","type":"facility","lineage":["https://openalex.org/I201448701","https://openalex.org/I4210138199"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"J. Nathan Kutz","raw_affiliation_strings":["Department of Applied Mathematics, University of Washington, Seattle, Washington, 98195, USA; Department of Electrical and Computer Engineering, University of Washington, Seattle, Washington, 98195, USA; Autodesk Research, London, WC2N 4HN, United Kingdom. Electronic address: kutz@uw.edu"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Applied Mathematics, University of Washington, Seattle, Washington, 98195, USA; Department of Electrical and Computer Engineering, University of Washington, Seattle, Washington, 98195, USA; Autodesk Research, London, WC2N 4HN, United Kingdom. Electronic address: kutz@uw.edu","institution_ids":["https://openalex.org/I201448701","https://openalex.org/I4210138199"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5060988111","display_name":"Krithika Manohar","orcid":"https://orcid.org/0000-0002-1582-6767"},"institutions":[{"id":"https://openalex.org/I201448701","display_name":"University of Washington","ror":"https://ror.org/00cvxb145","country_code":"US","type":"education","lineage":["https://openalex.org/I201448701"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Krithika Manohar","raw_affiliation_strings":["Department of Mechanical Engineering, University of Washington, Seattle, Washington, 98195, USA. Electronic address: kmanohar@uw.edu"],"raw_orcid":"https://orcid.org/0000-0002-1582-6767","affiliations":[{"raw_affiliation_string":"Department of Mechanical Engineering, University of Washington, Seattle, Washington, 98195, USA. Electronic address: kmanohar@uw.edu","institution_ids":["https://openalex.org/I201448701"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5088566148"],"corresponding_institution_ids":["https://openalex.org/I201448701"],"apc_list":{"value":3350,"currency":"USD","value_usd":3350},"apc_paid":{"value":3350,"currency":"USD","value_usd":3350},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.57866985,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"202","issue":null,"first_page":"109031","last_page":"109031"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12611","display_name":"Neural Networks and Reservoir Computing","score":0.968500018119812,"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"}},"topics":[{"id":"https://openalex.org/T12611","display_name":"Neural Networks and Reservoir Computing","score":0.968500018119812,"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/T11206","display_name":"Model Reduction and Neural Networks","score":0.005200000014156103,"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/T10502","display_name":"Advanced Memory and Neural Computing","score":0.002400000113993883,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/model-predictive-control","display_name":"Model predictive control","score":0.6955000162124634},{"id":"https://openalex.org/keywords/reservoir-computing","display_name":"Reservoir computing","score":0.5669999718666077},{"id":"https://openalex.org/keywords/system-identification","display_name":"System identification","score":0.5188000202178955},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.505299985408783},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3953000009059906},{"id":"https://openalex.org/keywords/control-system","display_name":"Control system","score":0.38600000739097595}],"concepts":[{"id":"https://openalex.org/C172205157","wikidata":"https://www.wikidata.org/wiki/Q1782962","display_name":"Model predictive control","level":3,"score":0.6955000162124634},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6590999960899353},{"id":"https://openalex.org/C135796866","wikidata":"https://www.wikidata.org/wiki/Q7315328","display_name":"Reservoir computing","level":4,"score":0.5669999718666077},{"id":"https://openalex.org/C119247159","wikidata":"https://www.wikidata.org/wiki/Q1366192","display_name":"System identification","level":3,"score":0.5188000202178955},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.505299985408783},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.42829999327659607},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3953000009059906},{"id":"https://openalex.org/C17500928","wikidata":"https://www.wikidata.org/wiki/Q959968","display_name":"Control system","level":2,"score":0.38600000739097595},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3287000060081482},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.3269999921321869},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.32269999384880066},{"id":"https://openalex.org/C14641988","wikidata":"https://www.wikidata.org/wiki/Q7315329","display_name":"Reservoir modeling","level":2,"score":0.3222000002861023},{"id":"https://openalex.org/C133731056","wikidata":"https://www.wikidata.org/wiki/Q4917288","display_name":"Control engineering","level":1,"score":0.3151000142097473},{"id":"https://openalex.org/C58328972","wikidata":"https://www.wikidata.org/wiki/Q184609","display_name":"Expert system","level":2,"score":0.2766000032424927},{"id":"https://openalex.org/C167928553","wikidata":"https://www.wikidata.org/wiki/Q1376021","display_name":"Estimation theory","level":2,"score":0.2662000060081482},{"id":"https://openalex.org/C47446073","wikidata":"https://www.wikidata.org/wiki/Q5165890","display_name":"Control theory (sociology)","level":3,"score":0.26579999923706055},{"id":"https://openalex.org/C91575142","wikidata":"https://www.wikidata.org/wiki/Q1971426","display_name":"Optimal control","level":2,"score":0.26409998536109924}],"mesh":[{"descriptor_ui":"D000098412","descriptor_name":"Predictive Learning Models","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000098417","descriptor_name":"Long Short Term Memory","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000098437","descriptor_name":"Prediction Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D003198","descriptor_name":"Computer Simulation","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D017711","descriptor_name":"Nonlinear Dynamics","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false}],"locations_count":2,"locations":[{"id":"doi:10.1016/j.neunet.2026.109031","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.neunet.2026.109031","pdf_url":null,"source":{"id":"https://openalex.org/S123019304","display_name":"Neural Networks","issn_l":"0893-6080","issn":["0893-6080","1879-2782"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Neural Networks","raw_type":"journal-article"},{"id":"pmid:42143957","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/42143957","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Neural networks : the official journal of the International Neural Network Society","raw_type":"Journal Article"}],"best_oa_location":{"id":"doi:10.1016/j.neunet.2026.109031","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.neunet.2026.109031","pdf_url":null,"source":{"id":"https://openalex.org/S123019304","display_name":"Neural Networks","issn_l":"0893-6080","issn":["0893-6080","1879-2782"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Neural Networks","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/13","display_name":"Climate action","score":0.5706659555435181}],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W1723433682","https://openalex.org/W2031493867","https://openalex.org/W2064675550","https://openalex.org/W2102928860","https://openalex.org/W2120475512","https://openalex.org/W2132423763","https://openalex.org/W2138484437","https://openalex.org/W2765128778","https://openalex.org/W2884156131","https://openalex.org/W2948353014","https://openalex.org/W3011704954","https://openalex.org/W3012621877","https://openalex.org/W3122065728","https://openalex.org/W3158052247","https://openalex.org/W3189223768","https://openalex.org/W4285682826","https://openalex.org/W4312967145","https://openalex.org/W4381995888","https://openalex.org/W4406362189"],"related_works":[],"abstract_inverted_index":{"Model":[0],"predictive":[1],"control":[2,8,104,123,137,144,158,180],"(MPC),":[3],"widely":[4],"used":[5],"for":[6,37,80,118,219],"real-time":[7],"of":[9,45,68,206],"complex":[10,46,224],"dynamical":[11,29,91],"systems,":[12],"operates":[13],"by":[14,182,189],"repeatedly":[15],"solving":[16],"an":[17,204],"optimization":[18],"problem":[19],"over":[20,203],"a":[21,66,149],"receding":[22],"time":[23],"horizon.":[24],"Its":[25],"success":[26],"hinges":[27],"on":[28,58],"models":[30,44],"that":[31,111],"are":[32,48,78,197],"accurate":[33],"yet":[34],"efficient":[35,75,217],"enough":[36],"rapid":[38,97],"online":[39],"computation.":[40],"Frequently,":[41],"the":[42,140,157,161],"governing":[43],"systems":[47,225],"either":[49],"unknown":[50,231],"or":[51],"computationally":[52,74],"inefficient,":[53],"forcing":[54],"MPC":[55,151,174,177,222],"to":[56,102,184],"rely":[57],"data-driven":[59,116,221],"surrogate":[60],"models.":[61],"Echo":[62],"state":[63],"networks":[64,71],"(ESNs),":[65],"class":[67],"recurrent":[69],"neural":[70],"trained":[72],"through":[73],"ridge":[76],"regression,":[77],"well-suited":[79],"this":[81,107],"role":[82],"and":[83,99,145,186,201,230],"have":[84],"demonstrated":[85],"strong":[86],"forecasting":[87],"capabilities":[88],"in":[89,170,223],"chaotic":[90],"systems.":[92],"Their":[93],"architecture":[94],"naturally":[95],"supports":[96],"training":[98,228],"flexible":[100],"adaptation":[101],"varying":[103],"inputs.":[105],"In":[106],"work,":[108],"we":[109],"demonstrate":[110],"ESNs":[112,196,214],"serve":[113],"as":[114,129,190,192,215],"effective":[115],"surrogates":[117,154],"system":[119,142],"dynamics":[120],"under":[121],"diverse":[122],"scenarios,":[124],"outperforming":[125],"competing":[126],"architectures":[127,218],"such":[128],"long":[130],"short-term":[131],"memory":[132],"(LSTM)":[133],"networks.":[134],"On":[135],"challenging":[136],"benchmarks,":[138],"including":[139],"Lorenz":[141],"with":[143,152,226],"fluid":[146],"flow":[147],"past":[148],"cylinder,":[150],"ESN":[153],"consistently":[155],"achieves":[156],"objective,":[159],"whereas":[160],"next-best":[162],"considered":[163],"architecture,":[164],"LSTM-based":[165,173],"MPC,":[166],"frequently":[167],"fails.":[168],"Even":[169],"cases":[171],"where":[172],"succeeds,":[175],"ESN-based":[176],"reduces":[178],"average":[179],"cost":[181],"up":[183],"10%":[185],"decreases":[187],"variability":[188],"much":[191],"85%.":[193],"Beyond":[194],"performance,":[195],"significantly":[198],"more":[199],"sample-efficient":[200],"train":[202],"order":[205],"magnitude":[207],"faster":[208],"than":[209],"LSTMs.":[210],"These":[211],"results":[212],"establish":[213],"accurate,":[216],"scalable":[220],"limited":[227],"data":[229],"dynamics.":[232]},"counts_by_year":[],"updated_date":"2026-07-22T07:51:19.307946","created_date":"2026-05-10T00:00:00"}
