Application of deep galerkin methods to a carbon abatement problem

Detalhes bibliográficos
Autor(a) principal: Bregunci, João Paulo Martino
Data de Publicação: 2023
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Institucional do FGV (FGV Repositório Digital)
Texto Completo: https://hdl.handle.net/10438/34552
Resumo: O objetivo central desse trabalho é a aplicação de um método de um algoritmo de aprendizado de máquina chamado DGM (Deep Galerkin Method), a fim de resolver um problema de abatimento ótimo nas emissões de carbono fórmulado por Hambel, Kraft e Schwartz. Para esse fim foram provados os principais resultados de controle estocástico, foi apresentada a formulação original do DGM e foram explanadas as principais ideias do trabalho Optimal Carbon Abatement in a Stochastic Equilibrium Model no qual o problema de abatimento ótimo foi formulado como um problema de controle estocástico. A estrategia utilizada nesse trabalho, contudo pode ser reutilizada e reaproveitada para múltiplos outros problemas de controle estocástico.
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spelling Bregunci, João Paulo MartinoEscolas::EMApCruz Cancino, Hugo Alexander de laSouza, Max Oliveira deSaporito, Yuri Fahham2023-12-01T12:41:15Z2023-12-01T12:41:15Z2023-04-28https://hdl.handle.net/10438/34552O objetivo central desse trabalho é a aplicação de um método de um algoritmo de aprendizado de máquina chamado DGM (Deep Galerkin Method), a fim de resolver um problema de abatimento ótimo nas emissões de carbono fórmulado por Hambel, Kraft e Schwartz. Para esse fim foram provados os principais resultados de controle estocástico, foi apresentada a formulação original do DGM e foram explanadas as principais ideias do trabalho Optimal Carbon Abatement in a Stochastic Equilibrium Model no qual o problema de abatimento ótimo foi formulado como um problema de controle estocástico. A estrategia utilizada nesse trabalho, contudo pode ser reutilizada e reaproveitada para múltiplos outros problemas de controle estocástico.The central objective of this work is the application of a method of a machine learning algorithm called DGM (Deep Galerkin Method), in order to solve an optimal abatement problem in carbon emissions formulated by Hambel, Kraft and Schwartz [HKS21]. To this end, the main results of stochastic control were proved, it was presented the original formulation of the DGM and the main ideas of the work Optimal Carbon Abatement in a Stochastic Equilibrium Model in which the optimal abatement problem was formulated as a stochastic control problem. The strategy used in this work, however, can be reused and repurposed for multiple others stochastic control problems.engHJBDGMStochastic ControlMachine LearningOptimizationCarbono - ReduçãoTeoria do controle estocásticoAprendizado do computadorAprendizado profundo (Aprendizado do computador)Otimização matemáticaApplication of deep galerkin methods to a carbon abatement probleminfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisreponame:Repositório Institucional do FGV (FGV Repositório Digital)instname:Fundação Getulio Vargas (FGV)instacron:FGVinfo:eu-repo/semantics/openAccessORIGINALDissertacao_JoaoBregunci2.pdfDissertacao_JoaoBregunci2.pdfDissertation correct order pagesapplication/pdf1267743https://repositorio.fgv.br/bitstreams/360cb073-2544-437c-bee9-c1f4c7d76e11/download306df40e5b55a78970e704a2c803cdafMD53LICENSElicense.txtlicense.txttext/plain; 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dc.title.eng.fl_str_mv Application of deep galerkin methods to a carbon abatement problem
title Application of deep galerkin methods to a carbon abatement problem
spellingShingle Application of deep galerkin methods to a carbon abatement problem
Bregunci, João Paulo Martino
HJB
DGM
Stochastic Control
Machine Learning
Optimization
Carbono - Redução
Teoria do controle estocástico
Aprendizado do computador
Aprendizado profundo (Aprendizado do computador)
Otimização matemática
title_short Application of deep galerkin methods to a carbon abatement problem
title_full Application of deep galerkin methods to a carbon abatement problem
title_fullStr Application of deep galerkin methods to a carbon abatement problem
title_full_unstemmed Application of deep galerkin methods to a carbon abatement problem
title_sort Application of deep galerkin methods to a carbon abatement problem
author Bregunci, João Paulo Martino
author_facet Bregunci, João Paulo Martino
author_role author
dc.contributor.unidadefgv.por.fl_str_mv Escolas::EMAp
dc.contributor.member.none.fl_str_mv Cruz Cancino, Hugo Alexander de la
Souza, Max Oliveira de
dc.contributor.author.fl_str_mv Bregunci, João Paulo Martino
dc.contributor.advisor1.fl_str_mv Saporito, Yuri Fahham
contributor_str_mv Saporito, Yuri Fahham
dc.subject.eng.fl_str_mv HJB
DGM
Stochastic Control
Machine Learning
Optimization
topic HJB
DGM
Stochastic Control
Machine Learning
Optimization
Carbono - Redução
Teoria do controle estocástico
Aprendizado do computador
Aprendizado profundo (Aprendizado do computador)
Otimização matemática
dc.subject.bibliodata.por.fl_str_mv Carbono - Redução
Teoria do controle estocástico
Aprendizado do computador
Aprendizado profundo (Aprendizado do computador)
Otimização matemática
description O objetivo central desse trabalho é a aplicação de um método de um algoritmo de aprendizado de máquina chamado DGM (Deep Galerkin Method), a fim de resolver um problema de abatimento ótimo nas emissões de carbono fórmulado por Hambel, Kraft e Schwartz. Para esse fim foram provados os principais resultados de controle estocástico, foi apresentada a formulação original do DGM e foram explanadas as principais ideias do trabalho Optimal Carbon Abatement in a Stochastic Equilibrium Model no qual o problema de abatimento ótimo foi formulado como um problema de controle estocástico. A estrategia utilizada nesse trabalho, contudo pode ser reutilizada e reaproveitada para múltiplos outros problemas de controle estocástico.
publishDate 2023
dc.date.accessioned.fl_str_mv 2023-12-01T12:41:15Z
dc.date.available.fl_str_mv 2023-12-01T12:41:15Z
dc.date.issued.fl_str_mv 2023-04-28
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://hdl.handle.net/10438/34552
url https://hdl.handle.net/10438/34552
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.source.none.fl_str_mv reponame:Repositório Institucional do FGV (FGV Repositório Digital)
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institution FGV
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collection Repositório Institucional do FGV (FGV Repositório Digital)
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