基于动力学关联函数的熵产生下界
Lower bounds on entropy production from dynamical correlation functions
AI总结：
该研究针对部分观测和粗粒化系统的熵产生推断难题，基于粗粒化状态可观测量的双时间关联函数不对称性推导两种熵产生下界，适用于马尔可夫动力学或耦合过阻尼朗之万方程系统，并阐释了其优化与紧度。
AI中文摘要：
熵产生是随机热力学中的关键属性。对于部分观测和粗粒化系统，其推断颇具挑战性，通常依赖于已被证明的下界。我们基于可实验获取的粗粒化状态可观测量的双时间关联函数的不对称性，推导了两种此类下界。对于非平衡稳态，该下界对任意关联滞后量均有效；对于随时间变化的过程，其需满足滞后量趋于零的极限。这些下界适用于遵循马尔可夫动力学或耦合过阻尼朗之万方程组（基于某些潜在不可观测的描述层面）的任意系统。我们针对这两种动力学示例阐释了这些下界，并讨论了它们的优化及潜在紧度。
英文摘要：
Entropy production is a key property in stochastic thermodynamics. For partially observed and coarse-grained systems, its inference is challenging and typically rests on proven lower bounds. We derive two versions of such bounds based on the asymmetry of experimentally accessible two-time correlation functions of coarse-grained state observables. For non-equilibrium steady states, the bound is valid for arbitrary correlation lag. For time-dependent processes, it requires the limit of vanishing lag. These bounds hold true for any system that follows either a Markovian dynamics or a coupled set of overdamped Langevin equations on some underlying, unobservable level of description. We illustrate the bounds for both types of dynamics and discuss their optimization and potential tightness.
