科研之友
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摘要
This paper is devoted to studying dimensional reduction for slow-fast data assimilation driven by Gaussian noise via stochastic averaging. We apply an energy method to show that the probability density for the reduced lower-dimensional system approximates that for the original system in mean square. In other words, the reduced system filter thus effectively captures the filter of the original system.
