{"id":"https://openalex.org/W4220705858","doi":"https://doi.org/10.1137/20m1375541","title":"Effective Generation of Compressed Stationary Gaussian Fields","display_name":"Effective Generation of Compressed Stationary Gaussian Fields","publication_year":2022,"publication_date":"2022-03-01","ids":{"openalex":"https://openalex.org/W4220705858","doi":"https://doi.org/10.1137/20m1375541"},"language":"en","primary_location":{"id":"doi:10.1137/20m1375541","is_oa":false,"landing_page_url":"https://doi.org/10.1137/20m1375541","pdf_url":null,"source":{"id":"https://openalex.org/S2911293512","display_name":"SIAM/ASA Journal on Uncertainty Quantification","issn_l":"2166-2525","issn":["2166-2525"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320508","host_organization_name":"Society for Industrial and Applied Mathematics","host_organization_lineage":["https://openalex.org/P4310320508"],"host_organization_lineage_names":["Society for Industrial and Applied Mathematics"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"SIAM/ASA Journal on Uncertainty Quantification","raw_type":"journal-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/A5081478532","display_name":"Robert Sawko","orcid":"https://orcid.org/0000-0002-0235-9266"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Robert Sawko","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5034298521","display_name":"Ma\u0142gorzata J. Zimo\u0144","orcid":"https://orcid.org/0000-0001-6343-0233"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ma\u0142gorzata J. Zimo\u0144","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5081478532"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0852,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.33929755,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"10","issue":"1","first_page":"439","last_page":"452"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10770","display_name":"Soil Geostatistics and Mapping","score":0.9957000017166138,"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"}},"topics":[{"id":"https://openalex.org/T10770","display_name":"Soil Geostatistics and Mapping","score":0.9957000017166138,"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"}},{"id":"https://openalex.org/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9860000014305115,"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"}},{"id":"https://openalex.org/T11405","display_name":"Geophysics and Gravity Measurements","score":0.958299994468689,"subfield":{"id":"https://openalex.org/subfields/1910","display_name":"Oceanography"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/circulant-matrix","display_name":"Circulant matrix","score":0.9050700664520264},{"id":"https://openalex.org/keywords/truncation","display_name":"Truncation (statistics)","score":0.8153159618377686},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.7066226005554199},{"id":"https://openalex.org/keywords/compressed-sensing","display_name":"Compressed sensing","score":0.6002086997032166},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.6001631617546082},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.5514140129089355},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.5294625163078308},{"id":"https://openalex.org/keywords/matrix","display_name":"Matrix (chemical analysis)","score":0.5226747393608093},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.5184733271598816},{"id":"https://openalex.org/keywords/covariance","display_name":"Covariance","score":0.5143974423408508},{"id":"https://openalex.org/keywords/covariance-matrix","display_name":"Covariance matrix","score":0.5109047293663025},{"id":"https://openalex.org/keywords/compression","display_name":"Compression (physics)","score":0.4541080594062805},{"id":"https://openalex.org/keywords/random-matrix","display_name":"Random matrix","score":0.44118061661720276},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.44093987345695496},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4180285930633545},{"id":"https://openalex.org/keywords/eigenvalues-and-eigenvectors","display_name":"Eigenvalues and eigenvectors","score":0.1916426122188568},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.18001654744148254},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.16487514972686768},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.13181394338607788},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.12303131818771362}],"concepts":[{"id":"https://openalex.org/C115973184","wikidata":"https://www.wikidata.org/wiki/Q245457","display_name":"Circulant matrix","level":2,"score":0.9050700664520264},{"id":"https://openalex.org/C106195933","wikidata":"https://www.wikidata.org/wiki/Q7847935","display_name":"Truncation (statistics)","level":2,"score":0.8153159618377686},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.7066226005554199},{"id":"https://openalex.org/C124851039","wikidata":"https://www.wikidata.org/wiki/Q2665459","display_name":"Compressed sensing","level":2,"score":0.6002086997032166},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.6001631617546082},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.5514140129089355},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5294625163078308},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.5226747393608093},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.5184733271598816},{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.5143974423408508},{"id":"https://openalex.org/C185142706","wikidata":"https://www.wikidata.org/wiki/Q1134404","display_name":"Covariance matrix","level":2,"score":0.5109047293663025},{"id":"https://openalex.org/C180016635","wikidata":"https://www.wikidata.org/wiki/Q2712821","display_name":"Compression (physics)","level":2,"score":0.4541080594062805},{"id":"https://openalex.org/C64812099","wikidata":"https://www.wikidata.org/wiki/Q176604","display_name":"Random matrix","level":3,"score":0.44118061661720276},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.44093987345695496},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4180285930633545},{"id":"https://openalex.org/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.1916426122188568},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.18001654744148254},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.16487514972686768},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.13181394338607788},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.12303131818771362},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1137/20m1375541","is_oa":false,"landing_page_url":"https://doi.org/10.1137/20m1375541","pdf_url":null,"source":{"id":"https://openalex.org/S2911293512","display_name":"SIAM/ASA Journal on Uncertainty Quantification","issn_l":"2166-2525","issn":["2166-2525"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320508","host_organization_name":"Society for Industrial and Applied Mathematics","host_organization_lineage":["https://openalex.org/P4310320508"],"host_organization_lineage_names":["Society for Industrial and Applied Mathematics"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"SIAM/ASA Journal on Uncertainty Quantification","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320334632","display_name":"Science and Technology Facilities Council","ror":"https://ror.org/057g20z61"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W1552812455","https://openalex.org/W1837874438","https://openalex.org/W1901745588","https://openalex.org/W1981602517","https://openalex.org/W1982770560","https://openalex.org/W2012859145","https://openalex.org/W2014125835","https://openalex.org/W2050369325","https://openalex.org/W2069650072","https://openalex.org/W2079559649","https://openalex.org/W2080345115","https://openalex.org/W2081656419","https://openalex.org/W2129752419","https://openalex.org/W2153700325","https://openalex.org/W2162547718","https://openalex.org/W2237852048","https://openalex.org/W2530262280","https://openalex.org/W3098109227","https://openalex.org/W3122901086","https://openalex.org/W4243836691"],"related_works":["https://openalex.org/W2139378013","https://openalex.org/W2560470131","https://openalex.org/W2049656902","https://openalex.org/W2394404719","https://openalex.org/W2903415888","https://openalex.org/W4287113494","https://openalex.org/W2348308003","https://openalex.org/W3148564979","https://openalex.org/W2063363084","https://openalex.org/W4386392001"],"abstract_inverted_index":{"In":[0,54],"this":[1],"paper,":[2],"we":[3,38],"present":[4],"a":[5,18,40],"novel":[6],"approach":[7],"to":[8,22],"compression":[9],"of":[10,33,50,74],"two-dimensional":[11],"Gaussian":[12],"random":[13],"fields.":[14],"We":[15,68],"build":[16],"on":[17],"circulant":[19,36],"embedding":[20],"method":[21],"effectively":[23],"decompose":[24],"and":[25,48,72],"generate":[26],"sample":[27],"realizations.":[28],"By":[29],"employing":[30],"the":[31,34,63,70,75],"structure":[32],"resulting":[35],"matrix,":[37],"propose":[39],"truncation":[41],"algorithm":[42,76],"that":[43,62],"controls":[44],"energy":[45],"through":[46],"rank":[47],"values":[49],"retained":[51],"spectral":[52],"components.":[53],"contrast":[55],"with":[56,77],"naive":[57],"truncation,":[58],"such":[59],"construction":[60],"ensures":[61],"covariance":[64],"matrix":[65],"remains":[66],"realizable.":[67],"discuss":[69],"properties":[71],"efficiency":[73],"numerical":[78],"examples.":[79]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
