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

dc.creatorEsquivel, Hugo
dc.creatorPrakash, Arun
dc.creatorLin, Guang
dc.date2022-08-22T20:42:17Z
dc.date2024
dc.date2022-08-22T20:42:17Z
dc.date2022
dc.date.accessioned2023-10-03T18:55:54Z
dc.date.available2023-10-03T18:55:54Z
dc.identifierHugo 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.identifier0021-9991
dc.identifierhttps://hdl.handle.net/11323/9463
dc.identifierhttps://doi.org/10.1016/j.jcp.2022.111425
dc.identifier10.1016/j.jcp.2022.111425
dc.identifierCorporación Universidad de la Costa
dc.identifierREDICUC - Repositorio CUC
dc.identifierhttps://repositorio.cuc.edu.co/
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/9166277
dc.descriptionThe 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.
dc.format1 página
dc.formatapplication/pdf
dc.formatapplication/pdf
dc.languageeng
dc.publisherAcademic Press Inc.
dc.publisherUnited States
dc.relationJournal of Computational Physics
dc.relation467
dc.rightsAtribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)
dc.rights© 2022 Elsevier B.V.
dc.rightshttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rightsinfo:eu-repo/semantics/embargoedAccess
dc.rightshttp://purl.org/coar/access_right/c_f1cf
dc.sourcehttps://www.sciencedirect.com/science/article/pii/S0021999122004879?via%3Dihub
dc.subjectStochastic discontinuities
dc.subjectStochastic dynamical systems
dc.subjectUncertainty quantification
dc.subjectLong-time integration
dc.subjectStochastic flow map
dc.subjectMulti-element flow-driven spectral chaos (ME-FSC)
dc.titleMulti-element flow-driven spectral chaos (ME-FSC) method for uncertainty quantification of dynamical systems
dc.typeArtículo de revista
dc.typehttp://purl.org/coar/resource_type/c_6501
dc.typeText
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion
dc.typehttp://purl.org/redcol/resource_type/ART
dc.typeinfo:eu-repo/semantics/publishedVersion
dc.typehttp://purl.org/coar/version/c_ab4af688f83e57aa


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