Variance reduction for the equation-free simulation of multiscale stochastic systems

Anastasia Papavasiliou, Yannis Kevrekidis

Research output: Contribution to journalArticle

9 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)70-89
Number of pages20
JournalMultiscale Modeling and Simulation
Volume6
Issue number1
DOIs
StatePublished - Dec 1 2007

All Science Journal Classification (ASJC) codes

  • Chemistry(all)
  • Modeling and Simulation
  • Ecological Modeling
  • Physics and Astronomy(all)
  • Computer Science Applications

Keywords

  • Control variates
  • Equation-free
  • Projective integration
  • Stochastic multiscale methods
  • Variance reduction

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