Adaptive exponential explicit integrators for stochastic differential equations

Bibliographic Details
Main Author: Maio, Pablo Aguiar De
Publication Date: 2024
Format: Doctoral thesis
Language: eng
Source: Repositório Institucional do FGV (FGV Repositório Digital)
Download full: https://hdl.handle.net/10438/36342
Summary: Esta tese apresenta novos métodos adaptativos para equações diferenciais estocásticas (EDEs) com ruído aditivo. Introduzimos técnicas inovadoras para embutir esquemas numéricos exponenciais, resultando em dois novos esquemas numéricos explícitos, embutidos e A-estáveis: o esquema de Linearização Local (LL) embutido e o esquema LL RungeKutta embutido. Além disso, apresentamos técnicas de otimização para o algoritmo de Padé, a fim de calcular de forma eficiente as exponenciais de matrizes exigidas por esses esquemas. A tese também fornece uma formulação abrangente para integradores numéricos com passo adaptativo, oferecendo uma nova estratégia adaptativa que trata de forma eficaz EDEs com níveis de ruído variáveis ao longo do intervalo de integração. Esses desenvolvimentos resultam em integradores explícitos adaptativos A-estáveis que, em geral, proporcionam o mesmo nível de precisão dos esquemas adaptativos explícitos instáveis, sendo particularmente eficientes para EDEs rígidas e com um custo computacional significativamente menor do que suas contrapartes instáveis
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spelling Maio, Pablo Aguiar DeEscolas::EMApSaporito, Yuri FahhamCruz Cancino, Hugo Alexander de la2025-01-14T17:34:08Z2025-01-14T17:34:08Z2024-10-10https://hdl.handle.net/10438/36342Esta tese apresenta novos métodos adaptativos para equações diferenciais estocásticas (EDEs) com ruído aditivo. Introduzimos técnicas inovadoras para embutir esquemas numéricos exponenciais, resultando em dois novos esquemas numéricos explícitos, embutidos e A-estáveis: o esquema de Linearização Local (LL) embutido e o esquema LL RungeKutta embutido. Além disso, apresentamos técnicas de otimização para o algoritmo de Padé, a fim de calcular de forma eficiente as exponenciais de matrizes exigidas por esses esquemas. A tese também fornece uma formulação abrangente para integradores numéricos com passo adaptativo, oferecendo uma nova estratégia adaptativa que trata de forma eficaz EDEs com níveis de ruído variáveis ao longo do intervalo de integração. Esses desenvolvimentos resultam em integradores explícitos adaptativos A-estáveis que, em geral, proporcionam o mesmo nível de precisão dos esquemas adaptativos explícitos instáveis, sendo particularmente eficientes para EDEs rígidas e com um custo computacional significativamente menor do que suas contrapartes instáveisThis thesis presents new adaptive methods for stochastic differential equations (SDEs) with additive noise. We introduce innovative techniques for embedding exponential-based schemes, resulting in two novel embedded A-stable explicit numerical schemes: the embedded Local Linearization (LL) scheme and the embedded LL Runge-Kutta scheme. Additionally, we present optimization techniques for the Padé algorithm to efficiently compute the matrix exponentials required by these schemes. The thesis also provides a comprehensive framework for adaptive time-step numerical integrators, offering a new adaptive strategy that effectively manages SDEs with varying noise levels over the integration interval. These developments yield explicit A-stable adaptive integrators that in general provide the same level of accuracy of explicit unstable adaptive schemes, and are particularly efficient for stiff SDEs, providing much lower computational cost than its unstable counterparts.FGV-EMAp e CAPES.engStochastic differential equationsStochastic numerical methodsLocal linearization approachAdaptive time-step integratorsA-stabilityNumerical analysisEquações diferenciais estocásticasMétodos numéricos estocásticosMétodos de linearização localMatemáticaAnálise numéricaEquações diferenciais estocásticasAlgoritmosMatemáticaAdaptive exponential explicit integrators for stochastic differential equationsinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesisreponame:Repositório Institucional do FGV (FGV Repositório Digital)instname:Fundação Getulio Vargas (FGV)instacron:FGVinfo:eu-repo/semantics/openAccessORIGINALThesis - Pablo De Maio.pdfThesis - Pablo De 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dc.title.eng.fl_str_mv Adaptive exponential explicit integrators for stochastic differential equations
title Adaptive exponential explicit integrators for stochastic differential equations
spellingShingle Adaptive exponential explicit integrators for stochastic differential equations
Maio, Pablo Aguiar De
Stochastic differential equations
Stochastic numerical methods
Local linearization approach
Adaptive time-step integrators
A-stability
Numerical analysis
Equações diferenciais estocásticas
Métodos numéricos estocásticos
Métodos de linearização local
Matemática
Análise numérica
Equações diferenciais estocásticas
Algoritmos
Matemática
title_short Adaptive exponential explicit integrators for stochastic differential equations
title_full Adaptive exponential explicit integrators for stochastic differential equations
title_fullStr Adaptive exponential explicit integrators for stochastic differential equations
title_full_unstemmed Adaptive exponential explicit integrators for stochastic differential equations
title_sort Adaptive exponential explicit integrators for stochastic differential equations
author Maio, Pablo Aguiar De
author_facet Maio, Pablo Aguiar De
author_role author
dc.contributor.unidadefgv.por.fl_str_mv Escolas::EMAp
dc.contributor.member.none.fl_str_mv Saporito, Yuri Fahham
dc.contributor.author.fl_str_mv Maio, Pablo Aguiar De
dc.contributor.advisor1.fl_str_mv Cruz Cancino, Hugo Alexander de la
contributor_str_mv Cruz Cancino, Hugo Alexander de la
dc.subject.eng.fl_str_mv Stochastic differential equations
Stochastic numerical methods
Local linearization approach
Adaptive time-step integrators
A-stability
Numerical analysis
topic Stochastic differential equations
Stochastic numerical methods
Local linearization approach
Adaptive time-step integrators
A-stability
Numerical analysis
Equações diferenciais estocásticas
Métodos numéricos estocásticos
Métodos de linearização local
Matemática
Análise numérica
Equações diferenciais estocásticas
Algoritmos
Matemática
dc.subject.por.fl_str_mv Equações diferenciais estocásticas
Métodos numéricos estocásticos
Métodos de linearização local
dc.subject.area.por.fl_str_mv Matemática
dc.subject.bibliodata.por.fl_str_mv Análise numérica
Equações diferenciais estocásticas
Algoritmos
Matemática
description Esta tese apresenta novos métodos adaptativos para equações diferenciais estocásticas (EDEs) com ruído aditivo. Introduzimos técnicas inovadoras para embutir esquemas numéricos exponenciais, resultando em dois novos esquemas numéricos explícitos, embutidos e A-estáveis: o esquema de Linearização Local (LL) embutido e o esquema LL RungeKutta embutido. Além disso, apresentamos técnicas de otimização para o algoritmo de Padé, a fim de calcular de forma eficiente as exponenciais de matrizes exigidas por esses esquemas. A tese também fornece uma formulação abrangente para integradores numéricos com passo adaptativo, oferecendo uma nova estratégia adaptativa que trata de forma eficaz EDEs com níveis de ruído variáveis ao longo do intervalo de integração. Esses desenvolvimentos resultam em integradores explícitos adaptativos A-estáveis que, em geral, proporcionam o mesmo nível de precisão dos esquemas adaptativos explícitos instáveis, sendo particularmente eficientes para EDEs rígidas e com um custo computacional significativamente menor do que suas contrapartes instáveis
publishDate 2024
dc.date.issued.fl_str_mv 2024-10-10
dc.date.accessioned.fl_str_mv 2025-01-14T17:34:08Z
dc.date.available.fl_str_mv 2025-01-14T17:34:08Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/doctoralThesis
format doctoralThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://hdl.handle.net/10438/36342
url https://hdl.handle.net/10438/36342
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)
instname:Fundação Getulio Vargas (FGV)
instacron:FGV
instname_str Fundação Getulio Vargas (FGV)
instacron_str FGV
institution FGV
reponame_str Repositório Institucional do FGV (FGV Repositório Digital)
collection Repositório Institucional do FGV (FGV Repositório Digital)
bitstream.url.fl_str_mv https://repositorio.fgv.br/bitstreams/e26a97e5-7166-42d9-99b5-d1d1cec73d48/download
https://repositorio.fgv.br/bitstreams/befa0520-0d36-4b6b-a0fc-59f8513e08cf/download
https://repositorio.fgv.br/bitstreams/46500e65-126e-46d9-a0fb-b358c4a03207/download
https://repositorio.fgv.br/bitstreams/61aeaf30-5349-478a-8249-228dae0dcd32/download
bitstream.checksum.fl_str_mv 2835b9045ba56c8fe92204832fc897f1
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d0ccd93c91cdd4faca35a9cf10efb568
bitstream.checksumAlgorithm.fl_str_mv MD5
MD5
MD5
MD5
repository.name.fl_str_mv Repositório Institucional do FGV (FGV Repositório Digital) - Fundação Getulio Vargas (FGV)
repository.mail.fl_str_mv
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