论文标题
部分可观测时空混沌系统的无模型预测
Spectral fingerprints of non-equilibrium dynamics: The case of a Brownian gyrator
论文作者
论文摘要
当同一系统在平衡条件下演变时,或者将其驱动到平衡的驱动外时,例如将其某些组件连接到在不同温度下保持的热浴时。在这里,我们集中于这种系统的可分析解决方案和实验性相关的模型 - 所谓的布朗循环 - 一种二维纳米机械,平均在非平衡条件下进行系统旋转,而在非平衡条件下围绕原点旋转,而在平衡中没有净旋转。在此示例上,我们讨论了一个问题,是否有可能区分两种类型的行为,而不是根据组件轨迹的统计特性,而是基于它们各自的光谱密度。后者被广泛用于表征各种动态系统,并使用标准内置软件包根据数据定期计算。从这样的角度来看,我们询问功率谱密度是否具有针对非平衡行为特有的“指纹”特性。我们表明,确实可以通过分析两个组件的光谱密度之间在短频率极限下或以零频率评估的两个组件的光谱密度之间的互相关来最终区分平衡和非平衡动力学。我们的分析预测通过实验和数值结果证实,为分析非平衡动力学的新方向开辟了一个新方向。
The same system can exhibit a completely different dynamical behavior when it evolves in equilibrium conditions or when it is driven out-of-equilibrium by, e.g., connecting some of its components to heat baths kept at different temperatures. Here we concentrate on an analytically solvable and experimentally-relevant model of such a system -- the so-called Brownian gyrator -- a two-dimensional nanomachine that performs a systematic, on average, rotation around the origin under non-equilibrium conditions, while no net rotation takes place in equilibrium. On this example, we discuss a question whether it is possible to distinguish between two types of a behavior judging not upon the statistical properties of the trajectories of components, but rather upon their respective spectral densities. The latter are widely used to characterize diverse dynamical systems and are routinely calculated from the data using standard built-in packages. From such a perspective, we inquire whether the power spectral densities possess some "fingerprint" properties specific to the behavior in non-equilibrium. We show that indeed one can conclusively distinguish between equilibrium and non-equilibrium dynamics by analyzing the cross-correlations between the spectral densities of both components in the short frequency limit, or from the spectral densities of both components evaluated at zero frequency. Our analytical predictions, corroborated by experimental and numerical results, open a new direction for the analysis of a non-equilibrium dynamics.