{"id":"https://openalex.org/W4416249424","doi":"https://doi.org/10.1109/ijcnn64981.2025.11228588","title":"Direct-Modelling in Nuclear Fusion by Combining Data-Integration and Physics-Informed Neural Networks","display_name":"Direct-Modelling in Nuclear Fusion by Combining Data-Integration and Physics-Informed Neural Networks","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4416249424","doi":"https://doi.org/10.1109/ijcnn64981.2025.11228588"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn64981.2025.11228588","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11228588","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"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/A5052555990","display_name":"Riccardo Rossi","orcid":"https://orcid.org/0000-0003-4414-6119"},"institutions":[{"id":"https://openalex.org/I116067653","display_name":"University of Rome Tor Vergata","ror":"https://ror.org/02p77k626","country_code":"IT","type":"education","lineage":["https://openalex.org/I116067653"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Riccardo Rossi","raw_affiliation_strings":["Università Degli Studi di Roma \"Tor Vergata\",Department of Industrial Engineering,Roma,Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Università Degli Studi di Roma \"Tor Vergata\",Department of Industrial Engineering,Roma,Italy","institution_ids":["https://openalex.org/I116067653"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005343901","display_name":"M. Gelfusa","orcid":"https://orcid.org/0000-0001-5158-7292"},"institutions":[{"id":"https://openalex.org/I116067653","display_name":"University of Rome Tor Vergata","ror":"https://ror.org/02p77k626","country_code":"IT","type":"education","lineage":["https://openalex.org/I116067653"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Michela Gelfusa","raw_affiliation_strings":["Università Degli Studi di Roma \"Tor Vergata\",Department of Industrial Engineering,Roma,Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Università Degli Studi di Roma \"Tor Vergata\",Department of Industrial Engineering,Roma,Italy","institution_ids":["https://openalex.org/I116067653"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016469726","display_name":"Novella Rutigliano","orcid":"https://orcid.org/0009-0005-4350-4019"},"institutions":[{"id":"https://openalex.org/I116067653","display_name":"University of Rome Tor Vergata","ror":"https://ror.org/02p77k626","country_code":"IT","type":"education","lineage":["https://openalex.org/I116067653"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Novella Rutigliano","raw_affiliation_strings":["Università Degli Studi di Roma \"Tor Vergata\",Department of Industrial Engineering,Roma,Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Università Degli Studi di Roma \"Tor Vergata\",Department of Industrial Engineering,Roma,Italy","institution_ids":["https://openalex.org/I116067653"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007348092","display_name":"P. Gaudio","orcid":"https://orcid.org/0000-0003-0861-558X"},"institutions":[{"id":"https://openalex.org/I116067653","display_name":"University of Rome Tor Vergata","ror":"https://ror.org/02p77k626","country_code":"IT","type":"education","lineage":["https://openalex.org/I116067653"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Pasquale Gaudio","raw_affiliation_strings":["Università Degli Studi di Roma \"Tor Vergata\",Department of Industrial Engineering,Roma,Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Università Degli Studi di Roma \"Tor Vergata\",Department of Industrial Engineering,Roma,Italy","institution_ids":["https://openalex.org/I116067653"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5043953788","display_name":"A. Murari","orcid":"https://orcid.org/0000-0002-3932-3865"},"institutions":[{"id":"https://openalex.org/I4210105455","display_name":"Istituto Nazionale di Fisica Nucleare, Sezione di Padova","ror":"https://ror.org/00z34yn88","country_code":"IT","type":"facility","lineage":["https://openalex.org/I160013858","https://openalex.org/I4210105455"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Andrea Murari","raw_affiliation_strings":["Università di Padova, Acciaierie Venete SpA),Consorzio RFX (CNR, ENEA, INFN,Padova,Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Università di Padova, Acciaierie Venete SpA),Consorzio RFX (CNR, ENEA, INFN,Padova,Italy","institution_ids":["https://openalex.org/I4210105455"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.39906103,"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":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11206","display_name":"Model Reduction and Neural Networks","score":0.45579999685287476,"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"}},"topics":[{"id":"https://openalex.org/T11206","display_name":"Model Reduction and Neural Networks","score":0.45579999685287476,"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/T10346","display_name":"Magnetic confinement fusion research","score":0.11420000344514847,"subfield":{"id":"https://openalex.org/subfields/3106","display_name":"Nuclear and High Energy 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/T10597","display_name":"Nuclear reactor physics and engineering","score":0.04129999876022339,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"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/tokamak","display_name":"Tokamak","score":0.7217000126838684},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5612000226974487},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.5511999726295471},{"id":"https://openalex.org/keywords/nuclear-fusion","display_name":"Nuclear fusion","score":0.5491999983787537},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.539900004863739},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.5163999795913696},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.515500009059906},{"id":"https://openalex.org/keywords/fusion-power","display_name":"Fusion power","score":0.4864000082015991}],"concepts":[{"id":"https://openalex.org/C116515362","wikidata":"https://www.wikidata.org/wiki/Q188589","display_name":"Tokamak","level":3,"score":0.7217000126838684},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5742999911308289},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5612000226974487},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.5511999726295471},{"id":"https://openalex.org/C69285833","wikidata":"https://www.wikidata.org/wiki/Q13082","display_name":"Nuclear fusion","level":2,"score":0.5491999983787537},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.539900004863739},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.5163999795913696},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.515500009059906},{"id":"https://openalex.org/C24671956","wikidata":"https://www.wikidata.org/wiki/Q641442","display_name":"Fusion power","level":3,"score":0.4864000082015991},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4740999937057495},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.39980000257492065},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36320000886917114},{"id":"https://openalex.org/C55037315","wikidata":"https://www.wikidata.org/wiki/Q5421151","display_name":"Experimental data","level":2,"score":0.34040001034736633},{"id":"https://openalex.org/C541523362","wikidata":"https://www.wikidata.org/wiki/Q80877","display_name":"Nuclear reactor","level":2,"score":0.3386000096797943},{"id":"https://openalex.org/C160988588","wikidata":"https://www.wikidata.org/wiki/Q939954","display_name":"Magnetic confinement fusion","level":4,"score":0.33009999990463257},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.3050000071525574},{"id":"https://openalex.org/C82706917","wikidata":"https://www.wikidata.org/wiki/Q10251","display_name":"Plasma","level":2,"score":0.29980000853538513},{"id":"https://openalex.org/C115260700","wikidata":"https://www.wikidata.org/wiki/Q11408","display_name":"Magnetic field","level":2,"score":0.28610000014305115},{"id":"https://openalex.org/C2779281675","wikidata":"https://www.wikidata.org/wiki/Q7068046","display_name":"Nuclear data","level":3,"score":0.2833000123500824},{"id":"https://openalex.org/C93226319","wikidata":"https://www.wikidata.org/wiki/Q193137","display_name":"Differential (mechanical device)","level":2,"score":0.2531000077724457}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/ijcnn64981.2025.11228588","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11228588","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},{"id":"pmh:oai:art.torvergata.it:2108/446984","is_oa":false,"landing_page_url":"https://hdl.handle.net/2108/446984","pdf_url":null,"source":{"id":"https://openalex.org/S4306400993","display_name":"Cineca Institutional Research Information System (Tor Vergata University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I116067653","host_organization_name":"University of Rome Tor Vergata","host_organization_lineage":["https://openalex.org/I116067653"],"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":"info:eu-repo/semantics/conferenceObject"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W2014078287","https://openalex.org/W2067529211","https://openalex.org/W2093625674","https://openalex.org/W2563151519","https://openalex.org/W2899283552","https://openalex.org/W2954531949","https://openalex.org/W2982690081","https://openalex.org/W3008118574","https://openalex.org/W3038272016","https://openalex.org/W3084017095","https://openalex.org/W3088655247","https://openalex.org/W3163993681","https://openalex.org/W3211565461","https://openalex.org/W4206831263","https://openalex.org/W4213377513","https://openalex.org/W4220699563","https://openalex.org/W4220717841","https://openalex.org/W4286245986","https://openalex.org/W4293363567","https://openalex.org/W4298345156","https://openalex.org/W4385814202","https://openalex.org/W4387899225","https://openalex.org/W4390670152","https://openalex.org/W4391618332","https://openalex.org/W4392016840","https://openalex.org/W4392911481","https://openalex.org/W4394060385","https://openalex.org/W4394741624","https://openalex.org/W4400514168","https://openalex.org/W4401002150","https://openalex.org/W4405022796"],"related_works":[],"abstract_inverted_index":{"Research":[0],"on":[1,33],"nuclear":[2,28,38,78,107,168,296],"fusion":[3,29,39,79,169,297],"aims":[4],"to":[5,26,102,167,292],"develop":[6],"a":[7,43,55,64,76,212,221,228],"carbon-free,":[8],"sustainable,":[9],"limitless,":[10],"and":[11,18,49,89,115,152,177,223,231,242,260,277],"safe":[12],"energy":[13],"source":[14],"for":[15,164,183,285],"both":[16],"medium-":[17],"long-term":[19],"applications.":[20],"One":[21],"of":[22,75,208,218,265,295],"the":[23,69,73,206,216,262,266,283,293],"possible":[24],"ways":[25],"control":[27],"reactions":[30],"is":[31,170,181],"based":[32],"magnetic":[34,51,60],"confinement.":[35],"Tokamaks":[36],"are":[37,122,254],"reactors":[40],"that":[41,67,214,253],"generate":[42],"toroidal":[44],"field":[45,52],"by":[46,53,227,239],"external":[47],"coils":[48],"poloidal":[50],"inducing":[54],"plasma":[56,113,189],"current.":[57],"These":[58],"two":[59],"fields":[61],"behave":[62],"like":[63],"\"magnetic":[65],"cage\"":[66],"confines":[68],"hot":[70],"plasma.":[71],"Unfortunately,":[72],"development":[74,207],"commercial":[77],"reactor":[80],"still":[81,171],"requires":[82],"ongoing":[83],"experimental":[84,235,258],"investigations,":[85],"advanced":[86],"physical":[87,134],"modelling,":[88],"innovative":[90,179],"engineering":[91],"solutions.":[92],"To":[93],"address":[94],"these":[95],"challenges,":[96],"artificial":[97],"intelligence":[98],"has":[99],"been":[100,158],"applied":[101],"solve":[103],"various":[104],"issues":[105],"in":[106,172,188],"fusion,":[108],"including":[109],"diagnostic":[110,244],"data":[111,259],"processing,":[112],"control,":[114],"AI-assisted":[116],"engineering.A":[117],"particularly":[118],"promising":[119],"AI":[120],"approach":[121],"Physics-Informed":[123],"Neural":[124],"Networks":[125],"(PINNs).":[126],"PINNs":[127,156],"uniquely":[128],"integrate":[129],"data-driven":[130,200],"methodologies":[131],"with":[132,199,234,257],"foundational":[133],"principles,":[135],"enabling":[136],"new":[137],"capabilities":[138],"such":[139],"as":[140],"meshless":[141],"partial":[142],"differential":[143],"equation":[144],"(PDE)":[145],"solving,":[146],"domain":[147],"decomposition,":[148],"multi-scale":[149],"analysis,":[150],"super-resolution,":[151],"direct":[153],"modelling.":[154],"While":[155],"have":[157],"extensively":[159],"studied,":[160],"an":[161],"optimized":[162],"framework":[163],"their":[165],"application":[166],"its":[173,224,232],"infancy.":[174],"Therefore,":[175],"developing":[176],"proposing":[178],"implementations":[180],"essential":[182],"advancing":[184],"efficient":[185],"PINN-based":[186],"solutions":[187],"physics.This":[190],"study":[191],"demonstrates":[192],"how":[193],"combining":[194],"prior":[195],"information":[196],"about":[197],"physics":[198,264],"deep":[201],"learning":[202],"techniques":[203],"can":[204],"support":[205],"direct-modelling":[209],"algorithms,":[210],"i.e.":[211],"procedure":[213],"overcomes":[215],"process":[217],"iteratively":[219],"hypothesizing":[220],"model":[222],"parameters,":[225],"followed":[226],"numerical":[229],"simulation":[230],"validation":[233],"data.":[236],"Once":[237],"supported":[238],"sufficiently":[240],"comprehensive":[241],"realistic":[243,251],"information,":[245],"this":[246],"methodology":[247],"would":[248,272,280],"allow":[249],"deriving":[250],"models":[252],"automatically":[255],"calibrated":[256],"reflect":[261],"underlying":[263],"investigated":[267],"phenomena.":[268],"This":[269],"algorithmic":[270],"solution":[271],"not":[273],"only":[274],"enhance":[275],"performance":[276],"reliability":[278],"but":[279],"also":[281],"reduce":[282],"need":[284],"conventional":[286],"iterative":[287],"modelling":[288],"techniques,":[289],"contributing":[290],"significantly":[291],"advancement":[294],"research.":[298]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-11-14T00:00:00"}