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Physics-guided Graph Convolutional Deep Equilibrium Network for Environmental Data

 
cris.virtual.department#PLACEHOLDER_PARENT_METADATA_VALUE#
cris.virtual.orcid0000-0001-9300-5860
cris.virtualsource.department90f2bec3-f84d-4738-9103-ba2cd2f04cbc
cris.virtualsource.orcid90f2bec3-f84d-4738-9103-ba2cd2f04cbc
dc.contributor.authorRodrigo-Bonet, Esther
dc.contributor.authorDeligiannis, Nikolaos
dc.date.accessioned2024-12-21T17:09:31Z
dc.date.available2024-12-21T17:09:31Z
dc.date.issued2024
dc.description.wosFundingTextThis work was supported in part by the Research Foundation - Flanders (FWO hrough the Ph.D. Fellowship Strategic Basic Research under Project 1SC4521N, in part by IMEC under the AAA Project AI-based Air Quality Map and Analytics and in part by the Flemish Government, under the "Onderzoeksprogramma Artificiele Intelligentie (AI) Vlaanderen" programme.
dc.identifier.doi10.23919/EUSIPCO63174.2024.10715398
dc.identifier.eisbn978-9-4645-9361-7
dc.identifier.isbn979-8-3315-1977-3
dc.identifier.issn2076-1465
dc.identifier.urihttps://imec-publications.be/handle/20.500.12860/45008
dc.publisherIEEE
dc.source.beginpage987
dc.source.conference32nd European Signal Processing Conference (EUSIPCO)
dc.source.conferencedate2024-08-26
dc.source.conferencelocationLyon
dc.source.endpage991
dc.source.numberofpages5
dc.title

Physics-guided Graph Convolutional Deep Equilibrium Network for Environmental Data

dc.typeProceedings paper
dspace.entity.typePublication
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