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• W2079536072 abstract "We study the problem of simulating the slow observable of a multiscale diffusion process. In particular, we extend previous algorithms to the case where the simulation of the different scales cannot be uncoupled and we have no explicit knowledge of the drift or the variance of the multiscale diffusion. This is the case when the simulation data come from a black box “legacy code,” or possibly from a fine scale simulator (e.g., MD, kMC) which we want to effectively model as a diffusion process. We improve the algorithm, using the past simulations as control variates, in order to reduce the variance of the subsequent simulations." @default.
• W2079536072 created "2016-06-24" @default.
• W2079536072 creator A5036566464 @default.
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• W2079536072 date "2007-01-01" @default.
• W2079536072 modified "2023-09-26" @default.
• W2079536072 title "Variance Reduction for the Equation‐Free Simulation of Multiscale Stochastic Systems" @default.
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• W2079536072 doi "https://doi.org/10.1137/060650635" @default.
• W2079536072 hasPublicationYear "2007" @default.
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