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arXiv 2103.08816math.DSnlin.CD
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一维非稳定流形混沌吸引子线性响应的有效计算

Efficient computation of linear response of chaotic attractors with one-dimensional unstable manifolds

Nisha Chandramoorthy, Qiqi Wang

摘要:

本文提出了空间分裂灵敏度或S3算法,将Ruelle的线性响应公式转化为一个良好的条件遍历平均计算。我们证明了Ruelle公式的分解是可微的非稳定流形,我们假设是一维的。Ruelle公式的这种分解确保了所得项之一,稳定贡献,可以使用正则化的切线方程计算,类似于非混沌系统。剩余的一项,称为不稳定的贡献,是正规化和转换成一个有效的可计算的遍历平均。在这个过程中,我们开发了新的算法,这可能是有用的超越线性响应,计算i)一个基本的统计量,我们引入所谓的密度梯度,和ii)不稳定的衍生物的正则化切向量字段和不稳定的方向。我们证明,S3算法,它结合了这些计算成分,进入稳定和不稳定的贡献,收敛像Monte Carlo近似的Ruelle公式。因此,这里提出的算法是迈向成熟的应用程序的灵敏度分析在混沌系统的第一步,这样的应用程序已受到限制,由于缺乏长期的灵敏度。

英文摘要:

This paper presents the space-split sensitivity or the S3 algorithm to transform Ruelle's linear response formula into a well-conditioned ergodic-averaging computation. We prove a decomposition of Ruelle's formula that is differentiable on the unstable manifold, which we assume to be one-dimensional. This decomposition of Ruelle's formula ensures that one of the resulting terms, the stable contribution, can be computed using a regularized tangent equation, similar to in a non-chaotic system. The remaining term, known as the unstable contribution, is regularized and converted into an efficiently computable ergodic average. In this process, we develop new algorithms, which may be useful beyond linear response, to compute i) a fundamental statistical quantity we introduce called the density gradient, and ii) the unstable derivatives of the regularized tangent vector field and the unstable direction. We prove that the S3 algorithm, which combines these computational ingredients that enter the stable and unstable contribution, converges like a Monte Carlo approximation of Ruelle's formula. The algorithm presented here is hence a first step toward full-fledged applications of sensitivity analysis in chaotic systems, wherever such applications have been limited due to lack of availability of long-term sensitivities.

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