{"id":"https://openalex.org/W4206831315","doi":"https://doi.org/10.1109/bigdata52589.2021.9671627","title":"Using Neural Networks for Two Dimensional Scientific Data Compression","display_name":"Using Neural Networks for Two Dimensional Scientific Data Compression","publication_year":2021,"publication_date":"2021-12-15","ids":{"openalex":"https://openalex.org/W4206831315","doi":"https://doi.org/10.1109/bigdata52589.2021.9671627"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata52589.2021.9671627","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata52589.2021.9671627","pdf_url":null,"source":{"id":"https://openalex.org/S4363607718","display_name":"2021 IEEE International Conference on Big Data (Big Data)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Big Data (Big Data)","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/A5079186981","display_name":"Lucas Hayne","orcid":null},"institutions":[{"id":"https://openalex.org/I188538660","display_name":"University of Colorado Boulder","ror":"https://ror.org/02ttsq026","country_code":"US","type":"education","lineage":["https://openalex.org/I188538660"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Lucas Hayne","raw_affiliation_strings":["University of Colorado Boulder, Boulder, CO"],"affiliations":[{"raw_affiliation_string":"University of Colorado Boulder, Boulder, CO","institution_ids":["https://openalex.org/I188538660"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056768201","display_name":"John Clyne","orcid":"https://orcid.org/0000-0003-2788-9017"},"institutions":[{"id":"https://openalex.org/I107766831","display_name":"NSF National Center for Atmospheric Research","ror":"https://ror.org/05cvfcr44","country_code":"US","type":"facility","lineage":["https://openalex.org/I107766831","https://openalex.org/I1311060795","https://openalex.org/I2799356940","https://openalex.org/I4210141337","https://openalex.org/I4210150888"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"John Clyne","raw_affiliation_strings":["Nat\u2019l Center for Atmospheric Research, Boulder, CO","Nat'l Center for Atmospheric Research, Boulder, CO"],"affiliations":[{"raw_affiliation_string":"Nat\u2019l Center for Atmospheric Research, Boulder, CO","institution_ids":["https://openalex.org/I107766831"]},{"raw_affiliation_string":"Nat'l Center for Atmospheric Research, Boulder, CO","institution_ids":["https://openalex.org/I107766831"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5071014389","display_name":"Shaomeng Li","orcid":"https://orcid.org/0000-0001-8295-2248"},"institutions":[{"id":"https://openalex.org/I107766831","display_name":"NSF National Center for Atmospheric Research","ror":"https://ror.org/05cvfcr44","country_code":"US","type":"facility","lineage":["https://openalex.org/I107766831","https://openalex.org/I1311060795","https://openalex.org/I2799356940","https://openalex.org/I4210141337","https://openalex.org/I4210150888"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shaomeng Li","raw_affiliation_strings":["Nat\u2019l Center for Atmospheric Research, Boulder, CO","Nat'l Center for Atmospheric Research, Boulder, CO"],"affiliations":[{"raw_affiliation_string":"Nat\u2019l Center for Atmospheric Research, Boulder, CO","institution_ids":["https://openalex.org/I107766831"]},{"raw_affiliation_string":"Nat'l Center for Atmospheric Research, Boulder, CO","institution_ids":["https://openalex.org/I107766831"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5079186981"],"corresponding_institution_ids":["https://openalex.org/I188538660"],"apc_list":null,"apc_paid":null,"fwci":0.4574,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.76065668,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"2956","last_page":"2965"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10901","display_name":"Advanced Data Compression Techniques","score":0.9997000098228455,"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/T10901","display_name":"Advanced Data Compression Techniques","score":0.9997000098228455,"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/T11269","display_name":"Algorithms and Data Compression","score":0.9977999925613403,"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/T11181","display_name":"Advanced Data Storage Technologies","score":0.9890999794006348,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7356082201004028},{"id":"https://openalex.org/keywords/data-compression","display_name":"Data compression","score":0.7206606864929199},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6863529682159424},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.5777692198753357},{"id":"https://openalex.org/keywords/image-compression","display_name":"Image compression","score":0.5700637102127075},{"id":"https://openalex.org/keywords/compression","display_name":"Compression (physics)","score":0.4929349422454834},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.42853397130966187},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.4116821885108948},{"id":"https://openalex.org/keywords/computer-engineering","display_name":"Computer engineering","score":0.4116821885108948},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3713730573654175},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.34377598762512207},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.32324451208114624},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.21775373816490173},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.1902565062046051},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.10406652092933655}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7356082201004028},{"id":"https://openalex.org/C78548338","wikidata":"https://www.wikidata.org/wiki/Q2493","display_name":"Data compression","level":2,"score":0.7206606864929199},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6863529682159424},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.5777692198753357},{"id":"https://openalex.org/C13481523","wikidata":"https://www.wikidata.org/wiki/Q412438","display_name":"Image compression","level":4,"score":0.5700637102127075},{"id":"https://openalex.org/C180016635","wikidata":"https://www.wikidata.org/wiki/Q2712821","display_name":"Compression (physics)","level":2,"score":0.4929349422454834},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.42853397130966187},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.4116821885108948},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.4116821885108948},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3713730573654175},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.34377598762512207},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.32324451208114624},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.21775373816490173},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.1902565062046051},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.10406652092933655},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"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/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata52589.2021.9671627","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata52589.2021.9671627","pdf_url":null,"source":{"id":"https://openalex.org/S4363607718","display_name":"2021 IEEE International Conference on Big Data (Big Data)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Big Data (Big Data)","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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W1968011005","https://openalex.org/W1974991834","https://openalex.org/W2052440657","https://openalex.org/W2099563019","https://openalex.org/W2108598243","https://openalex.org/W2111122809","https://openalex.org/W2125634634","https://openalex.org/W2140196014","https://openalex.org/W2142265837","https://openalex.org/W2486202470","https://openalex.org/W2508074989","https://openalex.org/W2552465432","https://openalex.org/W2760568291","https://openalex.org/W2766438018","https://openalex.org/W2785562966","https://openalex.org/W2794996660","https://openalex.org/W2896969442","https://openalex.org/W2912174957","https://openalex.org/W2913710883","https://openalex.org/W2962676454","https://openalex.org/W2963149687","https://openalex.org/W2964098744","https://openalex.org/W2981613960","https://openalex.org/W3034136960","https://openalex.org/W3036682213","https://openalex.org/W3091266734","https://openalex.org/W3103841771","https://openalex.org/W3110055068","https://openalex.org/W4230811558","https://openalex.org/W4297659253","https://openalex.org/W4300059844","https://openalex.org/W6694251005","https://openalex.org/W6780365925","https://openalex.org/W6844719003"],"related_works":["https://openalex.org/W3153124080","https://openalex.org/W2129829718","https://openalex.org/W2521595930","https://openalex.org/W4243608781","https://openalex.org/W3165542721","https://openalex.org/W4313046148","https://openalex.org/W4378191574","https://openalex.org/W2161981399","https://openalex.org/W2612632602","https://openalex.org/W2595043295"],"abstract_inverted_index":{"Continual":[0],"advances":[1],"in":[2,48,93,170],"high-performance":[3],"computing":[4],"have":[5,101],"enabled":[6],"the":[7,34,49,126,179],"development":[8],"of":[9,16,20,37,51,84,128,139],"higher":[10,159],"resolution":[11],"and":[12,95,152,167],"more":[13],"realistic":[14],"simulations":[15],"a":[17,24],"wide":[18],"variety":[19],"scientific":[21,64,71,85,121,142,147,190],"phenomena.":[22],"As":[23],"result,":[25],"many":[26,63],"computational":[27],"science":[28],"communities":[29],"are":[30,66],"increasingly":[31],"constrained":[32],"by":[33],"massive":[35],"volumes":[36],"data":[38,54,72,86,148],"produced,":[39],"for":[40,79,108,181],"example,":[41],"strict":[42],"storage":[43],"constraints":[44],"often":[45,91],"force":[46],"reductions":[47],"number":[50],"output":[52,55],"variables,":[53],"frequency,":[56],"or":[57],"simulation":[58],"length.":[59],"Accordingly,":[60],"modelers":[61],"across":[62],"domains":[65],"beginning":[67],"to":[68,117,120,145],"adopt":[69],"purpose-built":[70],"compression":[73,87,99,134,149,187],"techniques":[74],"as":[75],"an":[76,129],"effective":[77],"mitigation":[78],"these":[80],"challenges.":[81],"The":[82],"origins":[83],"tools":[88],"every":[89],"so":[90],"lie":[92],"image":[94,110],"video":[96],"compression.":[97],"Recently,":[98],"researchers":[100],"achieved":[102],"state-of-the-art":[103,146],"performance":[104,127],"using":[105],"neural":[106,132,156,185],"networks":[107],"natural":[109],"compression,":[111],"but":[112],"this":[113,154],"achievement":[114],"has":[115],"yet":[116],"be":[118],"adapted":[119],"data.":[122,143,191],"This":[123,175],"paper":[124],"assesses":[125],"existing":[130],"autoencoder":[131],"network":[133,157,186],"algorithm":[135],"on":[136,188],"two":[137],"sets":[138],"two-dimensional":[140],"floating-point":[141,189],"Compared":[144],"algorithms":[150],"SZ":[151],"ZFP,":[153],"out-of-the-box":[155],"achieves":[158],"peak":[160],"signal-to-noise":[161],"ratios":[162],"at":[163],"low":[164],"bit":[165],"rates,":[166],"remains":[168],"competitive":[169],"controlling":[171],"maximum":[172],"point-wise":[173],"error.":[174],"preliminary":[176],"assessment":[177],"paves":[178],"way":[180],"future":[182],"research":[183],"into":[184]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}