Multi-element flow-driven spectral chaos (ME-FSC) method for uncertainty quantification of dynamical systems

The flow-driven spectral chaos (FSC) is a recently developed method for tracking and quantifying uncertainties in the long-time response of stochastic dynamical systems using the spectral approach. The method uses a novel concept called enriched stochastic flow maps as a means to construct an evolvi...

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Autores:
Esquivel, Hugo
Prakash, Arun
Lin, Guang
Tipo de recurso:
Article of journal
Fecha de publicación:
2022
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/9463
Acceso en línea:
https://hdl.handle.net/11323/9463
https://doi.org/10.1016/j.jcp.2022.111425
https://repositorio.cuc.edu.co/
Palabra clave:
Stochastic discontinuities
Stochastic dynamical systems
Uncertainty quantification
Long-time integration
Stochastic flow map
Multi-element flow-driven spectral chaos (ME-FSC)
Rights
embargoedAccess
License
Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)
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oai_identifier_str oai:repositorio.cuc.edu.co:11323/9463
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network_name_str REDICUC - Repositorio CUC
repository_id_str
dc.title.eng.fl_str_mv Multi-element flow-driven spectral chaos (ME-FSC) method for uncertainty quantification of dynamical systems
title Multi-element flow-driven spectral chaos (ME-FSC) method for uncertainty quantification of dynamical systems
spellingShingle Multi-element flow-driven spectral chaos (ME-FSC) method for uncertainty quantification of dynamical systems
Stochastic discontinuities
Stochastic dynamical systems
Uncertainty quantification
Long-time integration
Stochastic flow map
Multi-element flow-driven spectral chaos (ME-FSC)
title_short Multi-element flow-driven spectral chaos (ME-FSC) method for uncertainty quantification of dynamical systems
title_full Multi-element flow-driven spectral chaos (ME-FSC) method for uncertainty quantification of dynamical systems
title_fullStr Multi-element flow-driven spectral chaos (ME-FSC) method for uncertainty quantification of dynamical systems
title_full_unstemmed Multi-element flow-driven spectral chaos (ME-FSC) method for uncertainty quantification of dynamical systems
title_sort Multi-element flow-driven spectral chaos (ME-FSC) method for uncertainty quantification of dynamical systems
dc.creator.fl_str_mv Esquivel, Hugo
Prakash, Arun
Lin, Guang
dc.contributor.author.spa.fl_str_mv Esquivel, Hugo
Prakash, Arun
Lin, Guang
dc.subject.proposal.eng.fl_str_mv Stochastic discontinuities
Stochastic dynamical systems
Uncertainty quantification
Long-time integration
Stochastic flow map
Multi-element flow-driven spectral chaos (ME-FSC)
topic Stochastic discontinuities
Stochastic dynamical systems
Uncertainty quantification
Long-time integration
Stochastic flow map
Multi-element flow-driven spectral chaos (ME-FSC)
description The flow-driven spectral chaos (FSC) is a recently developed method for tracking and quantifying uncertainties in the long-time response of stochastic dynamical systems using the spectral approach. The method uses a novel concept called enriched stochastic flow maps as a means to construct an evolving finite-dimensional random function space that is both accurate and computationally efficient in time. In this paper, we present a multi-element version of the FSC method (the ME-FSC method for short) to tackle (mainly) those dynamical systems that are inherently discontinuous over the probability space. In ME-FSC, the random domain is partitioned into several elements, and then the problem is solved separately on each random element using the FSC method. Subsequently, results are aggregated to compute the probability moments of interest using the law of total probability. To demonstrate the effectiveness of the ME-FSC method in dealing with discontinuities and long-time integration of stochastic dynamical systems, four representative numerical examples are presented in this paper, including the Van-der-Pol oscillator problem and the Kraichnan-Orszag three-mode problem. Results show that the ME-FSC method is capable of solving problems that have strong nonlinear dependencies over the probability space, both reliably and at low computational cost.
publishDate 2022
dc.date.accessioned.none.fl_str_mv 2022-08-22T20:42:17Z
dc.date.available.none.fl_str_mv 2022-08-22T20:42:17Z
2024
dc.date.issued.none.fl_str_mv 2022
dc.type.spa.fl_str_mv Artículo de revista
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dc.identifier.citation.spa.fl_str_mv Hugo Esquivel, Arun Prakash, Guang Lin, Multi-element flow-driven spectral chaos (ME-FSC) method for uncertainty quantification of dynamical systems, Journal of Computational Physics, Volume 467, 2022, 111425, ISSN 0021-9991, https://doi.org/10.1016/j.jcp.2022.111425.
dc.identifier.issn.spa.fl_str_mv 0021-9991
dc.identifier.uri.spa.fl_str_mv https://hdl.handle.net/11323/9463
dc.identifier.url.spa.fl_str_mv https://doi.org/10.1016/j.jcp.2022.111425
dc.identifier.doi.spa.fl_str_mv 10.1016/j.jcp.2022.111425
dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
dc.identifier.reponame.spa.fl_str_mv REDICUC - Repositorio CUC
dc.identifier.repourl.spa.fl_str_mv https://repositorio.cuc.edu.co/
identifier_str_mv Hugo Esquivel, Arun Prakash, Guang Lin, Multi-element flow-driven spectral chaos (ME-FSC) method for uncertainty quantification of dynamical systems, Journal of Computational Physics, Volume 467, 2022, 111425, ISSN 0021-9991, https://doi.org/10.1016/j.jcp.2022.111425.
0021-9991
10.1016/j.jcp.2022.111425
Corporación Universidad de la Costa
REDICUC - Repositorio CUC
url https://hdl.handle.net/11323/9463
https://doi.org/10.1016/j.jcp.2022.111425
https://repositorio.cuc.edu.co/
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.ispartofjournal.spa.fl_str_mv Journal of Computational Physics
dc.relation.citationvolume.spa.fl_str_mv 467
dc.rights.spa.fl_str_mv Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)
© 2022 Elsevier B.V.
dc.rights.uri.spa.fl_str_mv https://creativecommons.org/licenses/by-nc-nd/4.0/
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rights_invalid_str_mv Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)
© 2022 Elsevier B.V.
https://creativecommons.org/licenses/by-nc-nd/4.0/
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eu_rights_str_mv embargoedAccess
dc.format.extent.spa.fl_str_mv 1 página
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dc.publisher.spa.fl_str_mv Academic Press Inc.
dc.publisher.place.spa.fl_str_mv United States
institution Corporación Universidad de la Costa
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spelling Esquivel, HugoPrakash, ArunLin, Guang2022-08-22T20:42:17Z20242022-08-22T20:42:17Z2022Hugo Esquivel, Arun Prakash, Guang Lin, Multi-element flow-driven spectral chaos (ME-FSC) method for uncertainty quantification of dynamical systems, Journal of Computational Physics, Volume 467, 2022, 111425, ISSN 0021-9991, https://doi.org/10.1016/j.jcp.2022.111425.0021-9991https://hdl.handle.net/11323/9463https://doi.org/10.1016/j.jcp.2022.11142510.1016/j.jcp.2022.111425Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/The flow-driven spectral chaos (FSC) is a recently developed method for tracking and quantifying uncertainties in the long-time response of stochastic dynamical systems using the spectral approach. The method uses a novel concept called enriched stochastic flow maps as a means to construct an evolving finite-dimensional random function space that is both accurate and computationally efficient in time. In this paper, we present a multi-element version of the FSC method (the ME-FSC method for short) to tackle (mainly) those dynamical systems that are inherently discontinuous over the probability space. In ME-FSC, the random domain is partitioned into several elements, and then the problem is solved separately on each random element using the FSC method. Subsequently, results are aggregated to compute the probability moments of interest using the law of total probability. To demonstrate the effectiveness of the ME-FSC method in dealing with discontinuities and long-time integration of stochastic dynamical systems, four representative numerical examples are presented in this paper, including the Van-der-Pol oscillator problem and the Kraichnan-Orszag three-mode problem. 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