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arXiv 2402.11976nlin.CD
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非平衡态的混沌场是观测量无关的

Chaotic fields out of equilibrium are observable independent

Domenico Lippolis

AI总结:

本文证明了一类广泛适用的非平衡态混沌模型在向稳态弛豫时,其相空间分布对所有前推或积分观测量均通用,且由传输算子的主导及次主导本征函数决定,打破了统计特性依赖初始条件和观测量的传统观点。

摘要:

混沌动力学总是以大量不稳定轨迹的集合为特征,这些轨迹单独看是不可预测的,因此通常采用统计方法进行研究。相空间密度通常以指数级速度快速弛豫到一个极限分布,该分布决定了所有感兴趣观测量的长时间平均值。在这个渐近时间尺度之前,人们通常认为混沌的统计特性既依赖于初始条件,也依赖于所选的观测量。我证明了对一类广泛适用的模型而言情况并非如此:当系统仍在向统计平衡或稳态弛豫时,该类模型具有一个对所有前推或积分观测量都通用的相空间(“场”)分布。这一普适的分布轮廓由传输算子(Koopman 或 Perron-Frobenius)的主导和次主导本征函数共同决定,该算子负责在时间上将相空间密度向前或向后映射。

英文摘要:

Chaotic dynamics is always characterized by swarms of unstable trajectories, unpredictable individually, and thus generally studied statistically. It is often the case that such phase-space densities relax exponentially fast to a limiting distribution, that rules the long-time average of every observable of interest. Before that asymptotic timescale, the statistics of chaos is generally believed to depend on both the initial conditions and the chosen observable. I show that this is not the case for a widely applicable class of models, that feature a phase-space (`field') distribution common to all pushed-forward or integrated observables, while the system is still relaxing towards statistical equilibrium or a steady state. This universal profile is determined by both leading and first subleading eigenfunctions of the transport operator (Koopman or Perron-Frobenius) that maps phase-space densities forward or backward in time.

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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