{"id":"https://openalex.org/W4393406718","doi":"https://doi.org/10.1109/ciss59072.2024.10480207","title":"Physics-Informed Deep Learning with Kalman Filter Mixture: A New State Prediction Model","display_name":"Physics-Informed Deep Learning with Kalman Filter Mixture: A New State Prediction Model","publication_year":2024,"publication_date":"2024-03-13","ids":{"openalex":"https://openalex.org/W4393406718","doi":"https://doi.org/10.1109/ciss59072.2024.10480207"},"language":"en","primary_location":{"id":"doi:10.1109/ciss59072.2024.10480207","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ciss59072.2024.10480207","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 58th Annual Conference on Information Sciences and Systems (CISS)","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/A5046000778","display_name":"Niharika Deshpande","orcid":null},"institutions":[{"id":"https://openalex.org/I81365321","display_name":"Old Dominion University","ror":"https://ror.org/04zjtrb98","country_code":"US","type":"education","lineage":["https://openalex.org/I81365321"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Niharika Deshpande","raw_affiliation_strings":["Old Dominion University,Engineering Management & Systems Engineering,Norfolk,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Old Dominion University,Engineering Management & Systems Engineering,Norfolk,USA","institution_ids":["https://openalex.org/I81365321"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5090426992","display_name":"Hyoshin Park","orcid":"https://orcid.org/0000-0002-1490-5404"},"institutions":[{"id":"https://openalex.org/I81365321","display_name":"Old Dominion University","ror":"https://ror.org/04zjtrb98","country_code":"US","type":"education","lineage":["https://openalex.org/I81365321"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hyoshin Park","raw_affiliation_strings":["Old Dominion University,Engineering Management & Systems Engineering,Norfolk,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Old Dominion University,Engineering Management & Systems Engineering,Norfolk,USA","institution_ids":["https://openalex.org/I81365321"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I81365321"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.03130623,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9052000045776367,"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/T10320","display_name":"Neural Networks and Applications","score":0.9052000045776367,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/kalman-filter","display_name":"Kalman filter","score":0.8485563397407532},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.5714789032936096},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5554034113883972},{"id":"https://openalex.org/keywords/state","display_name":"State (computer science)","score":0.5441296100616455},{"id":"https://openalex.org/keywords/ensemble-kalman-filter","display_name":"Ensemble Kalman filter","score":0.48308899998664856},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.48030632734298706},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4400537610054016},{"id":"https://openalex.org/keywords/extended-kalman-filter","display_name":"Extended Kalman filter","score":0.439919114112854},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.2179575264453888},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.21296623349189758}],"concepts":[{"id":"https://openalex.org/C157286648","wikidata":"https://www.wikidata.org/wiki/Q846780","display_name":"Kalman filter","level":2,"score":0.8485563397407532},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.5714789032936096},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5554034113883972},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.5441296100616455},{"id":"https://openalex.org/C79334102","wikidata":"https://www.wikidata.org/wiki/Q3072268","display_name":"Ensemble Kalman filter","level":4,"score":0.48308899998664856},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.48030632734298706},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4400537610054016},{"id":"https://openalex.org/C206833254","wikidata":"https://www.wikidata.org/wiki/Q5421817","display_name":"Extended Kalman filter","level":3,"score":0.439919114112854},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2179575264453888},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.21296623349189758}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ciss59072.2024.10480207","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ciss59072.2024.10480207","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 58th Annual Conference on Information Sciences and Systems (CISS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Climate action","score":0.4000000059604645,"id":"https://metadata.un.org/sdg/13"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W2020934227","https://openalex.org/W2038250306","https://openalex.org/W2054091988","https://openalex.org/W2064675550","https://openalex.org/W2111880649","https://openalex.org/W2572939427","https://openalex.org/W2756203131","https://openalex.org/W2953509133","https://openalex.org/W2964199361","https://openalex.org/W3103720336","https://openalex.org/W3158304688","https://openalex.org/W3183282730","https://openalex.org/W4282963415","https://openalex.org/W4288109630","https://openalex.org/W4363647278","https://openalex.org/W6746015598","https://openalex.org/W6767460315","https://openalex.org/W6780221082","https://openalex.org/W6786706182"],"related_works":["https://openalex.org/W2119007379","https://openalex.org/W4206024512","https://openalex.org/W2525014937","https://openalex.org/W2103062922","https://openalex.org/W2162299404","https://openalex.org/W2014787281","https://openalex.org/W2053335193","https://openalex.org/W2394115825","https://openalex.org/W4233345519","https://openalex.org/W2130896823"],"abstract_inverted_index":{"Harnessing":[0],"a":[1,106,227,231],"range":[2],"of":[3,182,213,230],"modeling":[4,35],"approaches":[5],"like":[6],"Machine":[7],"Learning":[8,11],"(ML),":[9],"Deep":[10],"(DL)":[12],"etc.":[13],"for":[14,28,47,132,250],"analyzing":[15],"spatiotemporal":[16,38,57],"traffic":[17,232,262],"data":[18,140,149,263],"facilitates":[19],"precise":[20],"forecasts,":[21],"optimizing":[22],"transportation":[23,130],"planning":[24],"and":[25,126,136,153],"congestion":[26],"management":[27],"improved":[29],"efficiency.":[30],"Numerous":[31],"studies":[32],"in":[33,70,77,105,138,174,191],"prediction":[34,252],"diligently":[36],"incorporate":[37],"correlations":[39],"into":[40],"their":[41],"analyses":[42],"but":[43],"fail":[44],"to":[45,62,85,155,178,225,246],"account":[46],"epistemic":[48],"uncertainty":[49],"which":[50,82],"arises":[51],"from":[52,99,150],"incomplete":[53],"knowledge":[54],"across":[55],"different":[56,151],"scales.":[58],"This":[59,119,187],"study":[60,237],"aims":[61],"address":[63],"this":[64,100,216],"issue":[65],"by":[66,73,196,200],"capturing":[67,133],"unobserved":[68,164],"heterogeneity":[69],"travel":[71,158],"time":[72,159],"considering":[74],"distinct":[75],"peaks":[76],"the":[78,110,123,129,139,168,171,175,180,183,197,203,211,218,240,247,251,255,270],"probability":[79,88],"density":[80],"function,":[81],"we":[83],"refer":[84],"as":[86,170,244],"multimodal":[87,221],"distribution":[89],"while":[90],"establishing":[91],"causation":[92],"through":[93],"physics-based":[94],"principles.":[95],"The":[96,143],"information":[97],"obtained":[98],"methodology":[101],"is":[102],"then":[103],"employed":[104],"new":[107],"model":[108,121,173,193,249,267],"called":[109],"Physics":[111],"Informed-Graph":[112],"Convolutional":[113],"Gated":[114],"Recurrent":[115],"Neural":[116],"Network":[117],"(PI-GRNN).":[118],"DL":[120],"utilizes":[122],"inherent":[124],"structure":[125],"relationships":[127],"within":[128,254],"network":[131],"sequential":[134],"patterns":[135],"dependencies":[137],"over":[141],"time.":[142,186],"dynamic":[144,228],"graph-based":[145],"approach":[146,188,199,224],"can":[147],"utilize":[148],"locations":[152],"times":[154],"improve":[156],"future":[157],"predictions":[160,205],"at":[161],"distant":[162],"non-contiguous":[163],"locations.":[165],"We":[166],"employ":[167],"PI-GRNN":[169,204],"state-space":[172],"novel":[176],"KF":[177,256],"obtain":[179],"evolution":[181],"state":[184],"with":[185,206],"will":[189],"help":[190],"mitigating":[192],"drift":[194],"caused":[195],"data-driven":[198,220,242],"periodically":[201],"correcting":[202],"Kalman":[207],"filter":[208],"updates.":[209],"To":[210],"best":[212],"our":[214,266],"knowledge,":[215],"represents":[217],"pioneering":[219],"multivariate":[222],"learning":[223],"construct":[226],"graph":[229],"network.":[233],"Furthermore,":[234],"no":[235],"other":[236],"has":[238],"used":[239],"physics-informed":[241],"technique":[243],"opposed":[245],"mathematical":[248],"step":[253],"framework.":[257],"Extensive":[258],"experiments":[259],"on":[260],"real-world":[261],"demonstrate":[264],"that":[265],"consistently":[268],"outperforms":[269],"benchmark":[271],"models.":[272]},"counts_by_year":[],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}