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Model reduction for a class of singularly perturbed stochastic differential equations: Fast variable approximation

机译:一类奇摄动随机微分方程的模型简化:快速变量逼近

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We consider a class of stochastic differential equations in singular perturbation form, where the drift terms are linear and diffusion terms are nonlinear functions of the state variables. In our previous work, we approximated the slow variable dynamics of the original system by a reduced-order model when the singular perturbation parameter ϵ is small. In this work, we obtain an approximation for the fast variable dynamics. We prove that the first and second moments of the approximation are within an O(ϵ)-neighborhood of the first and second moments of the fast variable of the original system. The result holds for a finite time-interval after an initial transient has elapsed. We illustrate our results with a biomolecular system modeled by the chemical Langevin equation.
机译:我们考虑一类奇异摄动形式的随机微分方程,其中漂移项是线性的,扩散项是状态变量的非线性函数。在我们以前的工作中,当奇异摄动参数ϵ很小时,我们通过降阶模型来近似原始系统的慢变量动力学。在这项工作中,我们获得了快速变量动力学的近似值。我们证明了近似的第一和第二矩在原始系统快速变量的第一和第二矩的O(ϵ)邻域内。经过初始瞬变后,结果将保持有限的时间间隔。我们用化学朗格文方程建模的生物分子系统说明了我们的结果。

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