{"id":"https://openalex.org/W4282575325","doi":"https://doi.org/10.1109/pacificvis53943.2022.00012","title":"Scalar2Vec: Translating Scalar Fields to Vector Fields via Deep Learning","display_name":"Scalar2Vec: Translating Scalar Fields to Vector Fields via Deep Learning","publication_year":2022,"publication_date":"2022-04-01","ids":{"openalex":"https://openalex.org/W4282575325","doi":"https://doi.org/10.1109/pacificvis53943.2022.00012"},"language":"en","primary_location":{"id":"doi:10.1109/pacificvis53943.2022.00012","doi":"https://doi.org/10.1109/pacificvis53943.2022.00012","is_oa":false,"landing_page_url":"https://doi.org/10.1109/pacificvis53943.2022.00012","pdf_url":null,"source":{"id":"https://openalex.org/S4393918260","display_name":"IEEE Pacific Visualization Symposium","issn_l":"2165-8765","issn":["2165-8765"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":["jufo-1","norway-1"],"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":"2022 IEEE 15th Pacific Visualization Symposium (PacificVis)","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","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/A5020048077","display_name":"Pengfei Gu","orcid":"https://orcid.org/0000-0003-2160-1078","observed_orcids":["https://orcid.org/0000-0003-2160-1078"]},"institutions":[{"id":"https://openalex.org/I107639228","display_name":"University of Notre Dame","ror":"https://ror.org/00mkhxb43","country_code":"US","type":"education","lineage":["https://openalex.org/I107639228"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Pengfei Gu","raw_affiliation_strings":["University of Notre Dame"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Notre Dame","institution_ids":["https://openalex.org/I107639228"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073124436","display_name":"Jun Han","orcid":"https://orcid.org/0000-0002-7286-062X","observed_orcids":["https://orcid.org/0000-0002-7286-062X"]},"institutions":[{"id":"https://openalex.org/I107639228","display_name":"University of Notre Dame","ror":"https://ror.org/00mkhxb43","country_code":"US","type":"education","lineage":["https://openalex.org/I107639228"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jun Han","raw_affiliation_strings":["University of Notre Dame"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Notre Dame","institution_ids":["https://openalex.org/I107639228"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060901632","display_name":"Danny Z. Chen","orcid":"https://orcid.org/0000-0001-6565-2884","observed_orcids":["https://orcid.org/0000-0001-6565-2884"]},"institutions":[{"id":"https://openalex.org/I107639228","display_name":"University of Notre Dame","ror":"https://ror.org/00mkhxb43","country_code":"US","type":"education","lineage":["https://openalex.org/I107639228"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Danny Z. Chen","raw_affiliation_strings":["University of Notre Dame"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Notre Dame","institution_ids":["https://openalex.org/I107639228"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101913449","display_name":"Chaoli Wang","orcid":"https://orcid.org/0000-0002-0859-3619","observed_orcids":["https://orcid.org/0000-0002-0859-3619"]},"institutions":[{"id":"https://openalex.org/I107639228","display_name":"University of Notre Dame","ror":"https://ror.org/00mkhxb43","country_code":"US","type":"education","lineage":["https://openalex.org/I107639228"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chaoli Wang","raw_affiliation_strings":["University of Notre Dame"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Notre Dame","institution_ids":["https://openalex.org/I107639228"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I107639228"],"apc_list":null,"apc_paid":null,"fwci":3.1138,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.95097423,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"31","last_page":"40"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10481","display_name":"Computer Graphics and Visualization Techniques","score":0.9678000211715698,"subfield":{"id":"https://openalex.org/subfields/1704","display_name":"Computer Graphics and Computer-Aided Design"},"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/T10481","display_name":"Computer Graphics and Visualization Techniques","score":0.9678000211715698,"subfield":{"id":"https://openalex.org/subfields/1704","display_name":"Computer Graphics and Computer-Aided Design"},"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":0.01080000028014183,"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/T10719","display_name":"3D Shape Modeling and Analysis","score":0.005900000222027302,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"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/velocity-field","display_name":"velocity field","score":0.8799999952316284},{"id":"https://openalex.org/keywords/scientific-visualization","display_name":"scientific visualization","score":0.753000020980835},{"id":"https://openalex.org/keywords/multiscale-feature-extraction","display_name":"multi-scale feature extraction","score":0.7070000171661377}],"concepts":[{"id":"https://openalex.org/C57691317","wikidata":"https://www.wikidata.org/wiki/Q1289248","display_name":"Scalar (mathematics)","level":2,"score":0.7735322713851929},{"id":"https://openalex.org/C91188154","wikidata":"https://www.wikidata.org/wiki/Q186247","display_name":"Vector field","level":2,"score":0.7527044415473938},{"id":"https://openalex.org/C110521144","wikidata":"https://www.wikidata.org/wiki/Q193460","display_name":"Scalar field","level":2,"score":0.7273811101913452},{"id":"https://openalex.org/C37312099","wikidata":"https://www.wikidata.org/wiki/Q203484","display_name":"Vector Laplacian","level":4,"score":0.558850884437561},{"id":"https://openalex.org/C197657726","wikidata":"https://www.wikidata.org/wiki/Q174733","display_name":"Bipartite graph","level":3,"score":0.5563120245933533},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.5304296016693115},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5264701843261719},{"id":"https://openalex.org/C149364088","wikidata":"https://www.wikidata.org/wiki/Q185917","display_name":"Translation (biology)","level":4,"score":0.5154469609260559},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4785095751285553},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.45047080516815186},{"id":"https://openalex.org/C2027888","wikidata":"https://www.wikidata.org/wiki/Q521126","display_name":"Vector potential","level":3,"score":0.42037928104400635},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.420136034488678},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.38651466369628906},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2983582019805908},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.27992498874664307},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.1661471724510193},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.14946675300598145},{"id":"https://openalex.org/C115260700","wikidata":"https://www.wikidata.org/wiki/Q11408","display_name":"Magnetic field","level":2,"score":0.12803694605827332},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.08537578582763672},{"id":"https://openalex.org/C37914503","wikidata":"https://www.wikidata.org/wiki/Q156495","display_name":"Mathematical physics","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/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.0},{"id":"https://openalex.org/C105580179","wikidata":"https://www.wikidata.org/wiki/Q188928","display_name":"Messenger RNA","level":3,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/pacificvis53943.2022.00012","doi":"https://doi.org/10.1109/pacificvis53943.2022.00012","is_oa":false,"landing_page_url":"https://doi.org/10.1109/pacificvis53943.2022.00012","pdf_url":null,"source":{"id":"https://openalex.org/S4393918260","display_name":"IEEE Pacific Visualization Symposium","issn_l":"2165-8765","issn":["2165-8765"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":["jufo-1","norway-1"],"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":"2022 IEEE 15th Pacific Visualization Symposium (PacificVis)","raw_type":"proceedings-article"},{"id":"pmh:oai:repository.hkust.edu.hk:1783.1-142339","doi":null,"is_oa":false,"landing_page_url":"http://gateway.isiknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=LinksAMR&SrcApp=PARTNER_APP&DestLinkType=FullRecord&DestApp=WOS&KeyUT=000850180500004","pdf_url":null,"source":{"id":"https://openalex.org/S4306401796","display_name":"Rare & Special e-Zone (The Hong Kong University of Science and Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I200769079","host_organization_name":"Hong Kong University of Science and Technology","host_organization_lineage":["https://openalex.org/I200769079"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Conference paper"}],"best_oa_location":null,"sustainable_development_goals":[],"sustainable_development_goals_aurora":[],"study_designs":[],"awards":[{"id":"https://openalex.org/G8226214622","display_name":"CAREER: Effective Analysis, Exploration and Visualization of Big Flow Data to Understand Dynamic Flows","funder_award_id":"1455886","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"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":58,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1686810756","https://openalex.org/W1901129140","https://openalex.org/W1903029394","https://openalex.org/W1986200644","https://openalex.org/W2125389028","https://openalex.org/W2172509751","https://openalex.org/W2187089797","https://openalex.org/W2194775991","https://openalex.org/W2295537950","https://openalex.org/W2326925005","https://openalex.org/W2395611524","https://openalex.org/W2461158874","https://openalex.org/W2515788890","https://openalex.org/W2552465644","https://openalex.org/W2592480533","https://openalex.org/W2612063021","https://openalex.org/W2624820220","https://openalex.org/W2766540679","https://openalex.org/W2770705290","https://openalex.org/W2791402561","https://openalex.org/W2803398058","https://openalex.org/W2805862937","https://openalex.org/W2809852002","https://openalex.org/W2887240766","https://openalex.org/W2900545151","https://openalex.org/W2912174957","https://openalex.org/W2951798807","https://openalex.org/W2953096069","https://openalex.org/W2962530235","https://openalex.org/W2962793481","https://openalex.org/W2963201933","https://openalex.org/W2963417518","https://openalex.org/W2963890275","https://openalex.org/W2964128214","https://openalex.org/W2964256275","https://openalex.org/W2965010363","https://openalex.org/W2966298242","https://openalex.org/W2966925760","https://openalex.org/W2969947299","https://openalex.org/W2970971581","https://openalex.org/W2989676145","https://openalex.org/W3023287987","https://openalex.org/W3034583007","https://openalex.org/W3034838697","https://openalex.org/W3040462728","https://openalex.org/W3091859806","https://openalex.org/W3092668837","https://openalex.org/W3166382145","https://openalex.org/W3178576820","https://openalex.org/W3201452263","https://openalex.org/W3202983506","https://openalex.org/W4210655211","https://openalex.org/W4224081694","https://openalex.org/W4225396284","https://openalex.org/W4247939848","https://openalex.org/W4302059841","https://openalex.org/W6726029915"],"related_works":["https://openalex.org/W7018184794","https://openalex.org/W4224006294","https://openalex.org/W1997625724","https://openalex.org/W2100309672","https://openalex.org/W7096765660","https://openalex.org/W2951798807","https://openalex.org/W7098661656","https://openalex.org/W4284898543","https://openalex.org/W6991146080","https://openalex.org/W4210655211"],"abstract_inverted_index":{"We":[0,116],"introduce":[1],"Scalar2Vec,":[2],"a":[3,43,56],"new":[4],"deep":[5,138],"learning":[6,139],"solution":[7],"that":[8],"translates":[9],"scalar":[10,23,60,113],"fields":[11,15,83,104],"to":[12,49,75],"velocity":[13,28,102],"vector":[14,29,79,82,103],"for":[16,37,90],"scientific":[17],"visualization.":[18],"Given":[19],"multivariate":[20],"or":[21],"ensemble":[22],"field":[24,30,114],"volumes":[25,61],"and":[26,73,84,93,124,131],"their":[27,69,85,112],"counterparts,":[31],"Scalar2Vec":[32,98,121],"first":[33],"identifies":[34],"suitable":[35],"variables":[36],"scalar-to-vector":[38],"translation.":[39],"It":[40],"then":[41],"leverages":[42],"k-complete":[44],"bipartite":[45],"translation":[46,52],"network":[47,94],"(kCBT-Net)":[48],"complete":[50],"the":[51,63,77,106,118],"task.":[53],"kCBT-Net":[54],"takes":[55],"set":[57,109],"of":[58,62,105,120],"sampled":[59],"same":[64,107],"variable":[65],"as":[66],"input,":[67],"extracts":[68],"multi":[70],"-scale":[71],"information,":[72],"learns":[74],"synthesize":[76],"corresponding":[78],"volumes.":[80],"Ground-truth":[81],"derived":[86],"quantities":[87],"are":[88],"utilized":[89],"loss":[91],"computation":[92],"training.":[95],"After":[96],"training,":[97],"can":[99],"infer":[100],"unseen":[101],"data":[108,129],"directly":[110],"from":[111],"counterparts.":[115],"demonstrate":[117],"effectiveness":[119],"with":[122,134],"quantitative":[123],"qualitative":[125],"results":[126],"on":[127],"multiple":[128],"sets":[130],"compare":[132],"it":[133],"three":[135],"other":[136],"state-of-the-art":[137],"methods.":[140]},"counts_by_year":[{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":3}],"updated_date":"2026-10-17T09:40:24.721904","created_date":"2022-06-14T00:00:00"}