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arXiv math/0406201math.DScs.NAmath.NA
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小噪声极限下不变密度的收敛性

Convergence of invariant densities in the small-noise limit

Kevin K. Lin

AI总结:

本文通过数值研究,揭示了双曲性不同的动力系统在小噪声极限下不变密度的收敛速率常遵循幂律,并提出了一个启发式方法,在指数混合系统中能正确预测幂律指数,而在非指数混合系统中仅给出上界。

摘要:

本文系统数值研究了噪声对具有不同程度双曲性的动力系统的不变概率密度的影响。发现小噪声极限下不变密度的收敛速率常由幂律支配。此外,提出了一个简单的启发式方法,并发现其在指数混合系统中能正确预测幂律指数。在非指数混合系统中,该启发式方法仅给出幂律指数的上界。由于该数值研究需要计算超过2个数量级噪声振幅下的不变密度,本文也提供了讨论和比较计算不变概率密度的标准数值方法的机会。

英文摘要:

This paper presents a systematic numerical study of the effects of noise on the invariant probability densities of dynamical systems with varying degrees of hyperbolicity. It is found that the rate of convergence of invariant densities in the small-noise limit is frequently governed by power laws. In addition, a simple heuristic is proposed and found to correctly predict the power law exponent in exponentially mixing systems. In systems which are not exponentially mixing, the heuristic provides only an upper bound on the power law exponent. As this numerical study requires the computation of invariant densities across more than 2 decades of noise amplitudes, it also provides an opportunity to discuss and compare standard numerical methods for computing invariant probability densities.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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