揭示未解析事件中的非平衡态
Uncovering nonequilibrium from unresolved events
AI总结：
本研究提出基于事件分布与等待时间不对称性的无模型工具，用于检测非平衡行为并估计熵产生率，适用于多纤事件场景，并讨论了其在不同系统与状态下的适用性。
摘要：
与热力学定律密切相关，非平衡态的检测与量化对于揭示自然界的复杂性至关重要，尤其是那些隐藏在可观测层之下的复杂性。非平衡态热力学的理论发展采用粗粒化方法来考虑多种模拟实验限制的部分信息场景，从而能够推断诸如熵产生率等性质。一个普遍存在但尚未充分探索的场景涉及观测可能源于底层马尔可夫过程中多个跃迁的事件——我们称之为“多纤事件”——例如在粒子储库中测量的交换、隐马尔可夫模型、生物功能中的混合化学与机械转变、复合系统等情形。我们放宽了先前基于首通问题的框架中的一个主要假设，以评估多纤事件的非马尔可夫统计特性。通过利用事件分布及其等待时间的不对称性，我们提出了无模型工具来检测非平衡行为并估计熵产生，同时讨论了它们在不同类别系统和不同状态下的适用性，在这些情况下它们可能不提供新信息、提供非平衡的证据、提供熵产生的下界，甚至提供其精确值。结果通过解析和数值方法在参考模型中进行了说明。
英文摘要：
Closely related to the laws of thermodynamics, the detection and quantification of disequilibria are crucial in unraveling the complexities of nature, particularly those beneath observable layers. Theoretical developments in nonequilibrium thermodynamics employ coarse-graining methods to consider a diversity of partial information scenarios that mimic experimental limitations, allowing the inference of properties such as the entropy production rate. A ubiquitous but rather unexplored scenario involves observing events that can possibly arise from many transitions in the underlying Markov process--which we dub $\textit{multifilar events}$--as in the cases of exchanges measured at particle reservoirs, hidden Markov models, mixed chemical and mechanical transformations in biological function, composite systems, and more. We relax one of the main assumptions in a previously developed framework, based on first-passage problems, to assess the non-Markovian statistics of mutifilar events. By using the asymmetry of event distributions and their waiting-times, we put forward model-free tools to detect nonequilibrium behavior and estimate entropy production, while discussing their suitability for different classes of systems and regimes where they provide no new information, evidence of nonequilibrium, a lower bound for entropy production, or even its exact value. The results are illustrated in reference models through analytics and numerics.
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