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

2018/04/16 by Jeroen A. Wouters, Wouters, Jeroen, Georg A. Gottwald +1 · 1 citation
Computer Science · Physics and Astronomy · #Advanced Mathematical Modeling in Engineering #Chaotic Dynamics (nlin.CD) #FOS: Physical sciences #Model Reduction and Neural Networks #Scientific Research and Discoveries #Statistical Mechanics (cond-mat.stat-mech)

paper · pdf · doi:10.48550/arxiv.1804.09537

openalex publication_date 2018/04/16 · openalex created_date 2022/08/16 · openalex updated_date 2026/07/28

Abstract

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

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