Deep learning methods for high dimensional PDEs

dc.contributorJunca Peláez, Mauricio José
dc.contributorHoegele, Michael Anton
dc.contributorHernandez, Camilo
dc.creatorContreras Quiroz, Carlos Daniel
dc.date.accessioned2023-08-03T20:21:48Z
dc.date.accessioned2023-09-07T00:46:34Z
dc.date.available2023-08-03T20:21:48Z
dc.date.available2023-09-07T00:46:34Z
dc.date.created2023-08-03T20:21:48Z
dc.date.issued2023-07-02
dc.identifierhttp://hdl.handle.net/1992/69198
dc.identifierinstname:Universidad de los Andes
dc.identifierreponame:Repositorio Institucional Séneca
dc.identifierrepourl:https://repositorio.uniandes.edu.co/
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/8727782
dc.description.abstractIn this work we study numerical methods for partial differential equations in high dimensions using approximations with neural networks. We apply those methods to solve optimal control problems and N-agent games problems.
dc.languageeng
dc.publisherUniversidad de los Andes
dc.publisherMaestría en Matemáticas
dc.publisherFacultad de Ciencias
dc.publisherDepartamento de Matemáticas
dc.rightsAtribución 4.0 Internacional
dc.rightshttp://creativecommons.org/licenses/by/4.0/
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightshttp://purl.org/coar/access_right/c_abf2
dc.titleDeep learning methods for high dimensional PDEs
dc.typeTrabajo de grado - Maestría


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