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Physics-informed learning under epistemic uncertainty with an application to system health modeling

dc.contributor.authorSánchez Ramos, Luciano 
dc.contributor.authorCosta Cortéz, Nahuel Alejandro 
dc.contributor.authorOtero Rodríguez, José 
dc.contributor.authorCouso Blanco, Inés 
dc.date.accessioned2024-02-05T09:40:18Z
dc.date.available2024-02-05T09:40:18Z
dc.date.issued2023
dc.identifier.citationInternational Journal of Approximate Reasoning, 161 (2023) ; doi:10.1016/j.ijar.2023.108988
dc.identifier.issn0888-613X
dc.identifier.urihttps://hdl.handle.net/10651/71148
dc.description.sponsorshipSupported by Ministerio de Economía e Industria de España, grant PID2020-112726RB-I00 and by Principado de Asturias, grant SV-PA-21-AYUD/2021/50994spa
dc.language.isoengspa
dc.relation.ispartofInternational Journal of Approximate Reasoning, 161spa
dc.rightsAtribución 4.0 Internacional*
dc.rights© 2023 The Author(s). Published by Elsevier Inc
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.titlePhysics-informed learning under epistemic uncertainty with an application to system health modelingspa
dc.typejournal articlespa
dc.identifier.doi10.1016/j.ijar.2023.108988
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-112726RB-I00/ES/INTELIGENCIA COMPUTACIONAL PARA LA MITIGACION DE EMISIONES: NUEVAS METODOLOGIAS DE APRENDIZAJE CON DATOS INCOMPLETOS/ spa
dc.relation.projectIDSV-PA-21-AYUD/2021/50994spa
dc.relation.publisherversionhttps://doi.org/10.1016/j.ijar.2023.108988spa
dc.rights.accessRightsopen accessspa
dc.type.hasVersionVoRspa


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