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Stochastic model reduction for slow-fast systems with moderate time-scale separation

Wouters, J. and Gottwald, G. A. (2019) Stochastic model reduction for slow-fast systems with moderate time-scale separation. Multiscale Modeling and Simulation : a SIAM interdisciplinary journal, 17 (4). pp. 1172-1188. ISSN 1540-3459

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To link to this item DOI: 10.1137/18M1219965

Abstract/Summary

We propose a stochastic model reduction strategy for deterministic and stochastic slow-fast systems with finite time-scale separation. The stochastic model reduction relaxes the assumption of infinite time-scale separation of classical homogenization theory by incorporating deviations from this limit as described by an Edgeworth expansion. A surrogate system is constructed the parameters of which are matched to produce the same Edgeworth expansions up to any desired order of the original multi-scale system. We corroborate our analytical findings by numerical examples, showing significant improvements to classical homogenized model reduction.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Science > School of Mathematical, Physical and Computational Sciences > Department of Mathematics and Statistics
ID Code:81426
Uncontrolled Keywords:Condensed Matter - Statistical Mechanics, Nonlinear Sciences - Chaotic Dynamics
Publisher:Society for Industrial and Applied Mathematics

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