{"id":"https://openalex.org/W2293806930","doi":"https://doi.org/10.1145/2832080.2832084","title":"A parallel ensemble Kalman filter implementation based on modified Cholesky decomposition","display_name":"A parallel ensemble Kalman filter implementation based on modified Cholesky decomposition","publication_year":2015,"publication_date":"2015-11-11","ids":{"openalex":"https://openalex.org/W2293806930","doi":"https://doi.org/10.1145/2832080.2832084","mag":"2293806930"},"language":"en","primary_location":{"id":"doi:10.1145/2832080.2832084","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2832080.2832084","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 6th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems","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/A5037739760","display_name":"El\u00edas D. Ni\u00f1o-Ruiz","orcid":"https://orcid.org/0000-0001-7784-8163"},"institutions":[{"id":"https://openalex.org/I859038795","display_name":"Virginia Tech","ror":"https://ror.org/02smfhw86","country_code":"US","type":"education","lineage":["https://openalex.org/I859038795"]},{"id":"https://openalex.org/I142879360","display_name":"Universidad del Norte","ror":"https://ror.org/031e6xm45","country_code":"CO","type":"education","lineage":["https://openalex.org/I142879360"]}],"countries":["CO","US"],"is_corresponding":true,"raw_author_name":"Elias D. Nino-Ruiz","raw_affiliation_strings":["Virginia Tech Blacksburg and Universidad del Norte. Barranquilla, ATL, Colombia"],"affiliations":[{"raw_affiliation_string":"Virginia Tech Blacksburg and Universidad del Norte. Barranquilla, ATL, Colombia","institution_ids":["https://openalex.org/I142879360","https://openalex.org/I859038795"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008436217","display_name":"Adrian Sandu","orcid":"https://orcid.org/0000-0002-5380-0103"},"institutions":[{"id":"https://openalex.org/I859038795","display_name":"Virginia Tech","ror":"https://ror.org/02smfhw86","country_code":"US","type":"education","lineage":["https://openalex.org/I859038795"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Adrian Sandu","raw_affiliation_strings":["Virginia Tech Blacksburg"],"affiliations":[{"raw_affiliation_string":"Virginia Tech Blacksburg","institution_ids":["https://openalex.org/I859038795"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5003817085","display_name":"Xinwei Deng","orcid":"https://orcid.org/0000-0002-1560-2405"},"institutions":[{"id":"https://openalex.org/I859038795","display_name":"Virginia Tech","ror":"https://ror.org/02smfhw86","country_code":"US","type":"education","lineage":["https://openalex.org/I859038795"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xinwei Deng","raw_affiliation_strings":["Virginia Tech Blacksburg"],"affiliations":[{"raw_affiliation_string":"Virginia Tech Blacksburg","institution_ids":["https://openalex.org/I859038795"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5037739760"],"corresponding_institution_ids":["https://openalex.org/I142879360","https://openalex.org/I859038795"],"apc_list":null,"apc_paid":null,"fwci":1.5552,"has_fulltext":false,"cited_by_count":21,"citation_normalized_percentile":{"value":0.82686988,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"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/T10466","display_name":"Meteorological Phenomena and Simulations","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1902","display_name":"Atmospheric Science"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10466","display_name":"Meteorological Phenomena and Simulations","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1902","display_name":"Atmospheric Science"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10029","display_name":"Climate variability and models","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/2306","display_name":"Global and Planetary Change"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11371","display_name":"Wind and Air Flow Studies","score":0.9919999837875366,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/cholesky-decomposition","display_name":"Cholesky decomposition","score":0.9706474542617798},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.667270302772522},{"id":"https://openalex.org/keywords/kalman-filter","display_name":"Kalman filter","score":0.5945848226547241},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5782953500747681},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.5585045218467712},{"id":"https://openalex.org/keywords/covariance-matrix","display_name":"Covariance matrix","score":0.48977112770080566},{"id":"https://openalex.org/keywords/ensemble-kalman-filter","display_name":"Ensemble Kalman filter","score":0.48443344235420227},{"id":"https://openalex.org/keywords/covariance","display_name":"Covariance","score":0.48286157846450806},{"id":"https://openalex.org/keywords/extended-kalman-filter","display_name":"Extended Kalman filter","score":0.3293784558773041},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2517055571079254},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.14866483211517334},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.11395284533500671},{"id":"https://openalex.org/keywords/eigenvalues-and-eigenvectors","display_name":"Eigenvalues and eigenvectors","score":0.08045321702957153}],"concepts":[{"id":"https://openalex.org/C34727166","wikidata":"https://www.wikidata.org/wiki/Q515375","display_name":"Cholesky decomposition","level":3,"score":0.9706474542617798},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.667270302772522},{"id":"https://openalex.org/C157286648","wikidata":"https://www.wikidata.org/wiki/Q846780","display_name":"Kalman filter","level":2,"score":0.5945848226547241},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5782953500747681},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.5585045218467712},{"id":"https://openalex.org/C185142706","wikidata":"https://www.wikidata.org/wiki/Q1134404","display_name":"Covariance matrix","level":2,"score":0.48977112770080566},{"id":"https://openalex.org/C79334102","wikidata":"https://www.wikidata.org/wiki/Q3072268","display_name":"Ensemble Kalman filter","level":4,"score":0.48443344235420227},{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.48286157846450806},{"id":"https://openalex.org/C206833254","wikidata":"https://www.wikidata.org/wiki/Q5421817","display_name":"Extended Kalman filter","level":3,"score":0.3293784558773041},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2517055571079254},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.14866483211517334},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.11395284533500671},{"id":"https://openalex.org/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.08045321702957153},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/2832080.2832084","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2832080.2832084","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 6th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"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":16,"referenced_works":["https://openalex.org/W90026333","https://openalex.org/W333990585","https://openalex.org/W1964477602","https://openalex.org/W1987107073","https://openalex.org/W2009104157","https://openalex.org/W2043654662","https://openalex.org/W2105812312","https://openalex.org/W2130351130","https://openalex.org/W2149381887","https://openalex.org/W2161358768","https://openalex.org/W2314584550","https://openalex.org/W2951980816","https://openalex.org/W3101788651","https://openalex.org/W3169190375","https://openalex.org/W4232958218","https://openalex.org/W4233549502"],"related_works":["https://openalex.org/W2737320860","https://openalex.org/W2788965013","https://openalex.org/W2621521991","https://openalex.org/W4206024512","https://openalex.org/W1970833970","https://openalex.org/W3123211304","https://openalex.org/W3149332717","https://openalex.org/W2900391131","https://openalex.org/W2768858086","https://openalex.org/W1657752397"],"abstract_inverted_index":{"This":[0],"paper":[1],"discusses":[2],"an":[3],"efficient":[4],"parallel":[5,83,181,199],"implementation":[6,20,149,174,200],"of":[7,34,58,67,73,87,136,153,171,179,195,209],"the":[8,14,24,35,48,64,76,85,91,97,104,109,117,124,128,140,155,165,172,180,192,197,206,210],"ensemble":[9,158],"Kalman":[10,160],"filter":[11,161],"based":[12],"on":[13,53,127],"modified":[15,45],"Cholesky":[16,46],"decomposition.":[17],"The":[18,56,134,147,168],"proposed":[19,148,173,198,211],"starts":[21],"with":[22,123],"decomposing":[23],"domain":[25,106],"into":[26],"sub-domains.":[27],"In":[28],"each":[29],"sub-domain":[30],"a":[31,44],"sparse":[32],"estimation":[33,187],"inverse":[36],"background":[37],"error":[38],"covariance":[39,186],"matrix":[40],"is":[41,61,79,175,188,201],"computed":[42,51],"via":[43],"decomposition;":[47],"estimates":[49],"are":[50,95,100,114],"concurrently":[52],"separate":[54],"processors.":[55],"sparsity":[57],"this":[59],"estimator":[60],"dictated":[62],"by":[63],"conditional":[65],"independence":[66],"model":[68,166],"components":[69],"for":[70,163,191],"some":[71],"radius":[72],"influence.":[74],"Then,":[75],"assimilation":[77],"step":[78],"carried":[80],"out":[81],"in":[82,139,151],"without":[84],"need":[86],"inter-processor":[88],"communication.":[89],"Once":[90],"local":[92,157],"analysis":[93,98,110],"states":[94],"computed,":[96],"sub-domains":[99],"mapped":[101],"back":[102],"onto":[103],"global":[105],"to":[107,145,177],"obtain":[108],"ensemble.":[111],"Computational":[112],"experiments":[113,141],"performed":[115],"using":[116],"Atmospheric":[118],"General":[119],"Circulation":[120],"Model":[121],"(SPEEDY)":[122],"T-63":[125],"resolution":[126],"Blueridge":[129],"cluster":[130],"at":[131],"Virginia":[132],"Tech.":[133],"number":[135,194],"processors":[137],"used":[138],"ranges":[142],"from":[143],"96":[144],"2,048.":[146],"outperforms":[150],"terms":[152],"accuracy":[154],"well-known":[156],"transform":[159],"(LETKF)":[162],"all":[164],"variables.":[167],"computational":[169],"time":[170],"similar":[176],"that":[178],"LETKF":[182],"method":[183],"(where":[184],"no":[185],"performed).":[189],"Finally,":[190],"largest":[193],"processors,":[196],"400":[202],"times":[203],"faster":[204],"than":[205],"serial":[207],"version":[208],"method.":[212]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":5},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
