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dc.contributor.authorSieber, J
dc.contributor.authorMarschler, C
dc.contributor.authorStarke, J
dc.date.accessioned2018-09-13T12:00:28Z
dc.date.issued2018-11-08
dc.description.abstractA common approach to studying high-dimensional systems with emergent low-dimensional behavior is based on lift-evolve-restrict maps (called equation-free methods): first, a user-defined lifting operator maps a set of low-dimensional coordinates into the high-dimensional phase space, then the high-dimensional (microscopic) evolution is applied for some time, and finally a user-defined restriction operator maps down into a low-dimensional space again. We prove convergence of equation-free methods for finite time-scale separation with respect to a method parameter, the so-called healing time. Our convergence result justifies equation-free methods as a tool for performing high-level tasks such as bifurcation analysis on high-dimensional systems. More precisely, if the high-dimensional system has an attracting invariant manifold with smaller expansion and attraction rates in the tangential direction than in the transversal direction (normal hyperbolicity), and restriction and lifting satisfy some generic transversality conditions, then an implicit formulation of the lift-evolve-restrict procedure generates an approximate map that converges to the flow on the invariant manifold for healing time going to infinity. In contrast to all previous results, our result does not require the time scale separation to be large. A demonstration with Michaelis-Menten kinetics shows that the error estimates of our theorem are sharp. The ability to achieve convergence even for finite time scale separation is especially important for applications involving stochastic systems, where the evolution occurs at the level of distributions, governed by the Fokker-Planck equation. In these applications the spectral gap is typically finite. We investigate a low-dimensional stochastic differential equation where the ratio between the decay rates of fast and slow variables is 2.en_GB
dc.description.sponsorshipJ. Sieber’s research was supported by funding from the European Union’s Horizon 2020 research and innovation programme under Grant Agreement number 643073, by the EPSRC Centre for Predictive Modelling in Healthcare (Grant Number EP/N014391/1) and by the EPSRC Fellowship EP/N023544/1. C. Marschler and J. Starke would like to thank Civilingeniør Frederik Christiansens Almennyttige Fond for financial support. J. Starke would also like to thank the Villum Fonden (VKR-Centre of Excellence Ocean Life), the Technical University of Denmark and Queen Mary University of London for financial support.en_GB
dc.identifier.citationVol. 17 (4), pp. 2574-2614.en_GB
dc.identifier.doi10.1137/17M1126084
dc.identifier.urihttp://hdl.handle.net/10871/33985
dc.language.isoenen_GB
dc.publisherSociety for Industrial and Applied Mathematicsen_GB
dc.relation.urlhttps://doi.org/10.6084/m9.figshare.6166421en_GB
dc.rights© 2018, Society for Industrial and Applied Mathematics.
dc.subjectimplicit equation-free methodsen_GB
dc.subjectslow-fast systemsen_GB
dc.subjectstochastic differential equationsen_GB
dc.subjectMichaelis-Menten kineticsen_GB
dc.subjectdimension reductionen_GB
dc.titleConvergence of equation-free methods in the case of finite time scale separation with application to deterministic and stochastic systemsen_GB
dc.typeArticleen_GB
dc.descriptionThis is the author accepted manuscript. The final version is available from SIAM via the DOI in this record.en_GB
dc.description41 pages of supplementary material available at https://doi.org/10.6084/m9.figshare.6166421en_GB
dc.identifier.journalSIAM Journal on Applied Dynamical Systemsen_GB


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