论文标题
随机热力学中的耦合和隐藏的自由度
Coupled and Hidden Degrees of Freedom in Stochastic Thermodynamics
论文作者
论文摘要
本文研究了随机热力学理论中一个联合系统不同程度的自由度的相互作用。首先,给出了随机过程,信息理论和随机热力学理论的全面介绍,从而突出了关键结果。 在第二部分中,考虑了具有相互作用程度的自由度的系统。这允许研究碰撞浴的热化特性,即通过碰撞与局部系统相互作用的平衡颗粒。结果表明,系统和浴室之间的相互作用必须可逆,以确保系统的热化。此外,在相互作用系统的背景下,信息在热力学中的作用是在热力学中的。使用因果条件的概念,开发了一个框架来查找摄入耦合子系统相互影响的熵作品。该框架应用于通常在信息热力学中分别研究的各种设置。 第三部分涵盖了重要的系统变量被隐藏在观察中的情况。该问题是通过呈现微型威格默模型的动机,并表明其运动可以通过主动布朗运动近似。但是,该程序被大大低估了其能量耗散。结果表明,这是游泳机构是隐藏变量的事实的结果。随后,讨论了不同的有效描述方法,并将其应用于一个简单的模型系统,其中隐藏的慢速自由度对波动定理的影响。最后,研究了一个设置,在该设置中,可以通过将基本的隐藏的马尔可夫进程与可观察到的数据拟合,从仅部分观察系统动力学来为隐藏的熵产生提供界限。
This thesis investigates the interactions of different degrees of freedom of one joint system within the theory of stochastic thermodynamics. First, a comprehensive introduction to the subjects of stochastic processes, information theory and the theory of stochastic thermodynamics is given, thereby highlighting the key results. In the second part, systems with interacting degrees of freedom are considered. This allows investigation of thermalization properties of collisional baths, i.e. particles at equilibrium interacting with a localized system via collisions. It is shown that the interactions between system and bath must be reversible to ensure thermalization of the system. Moreover, the role of information in thermodynamics is presented and interpreted in the context of interacting systems. Using the concept of causal conditioning, a framework is developed for finding entropy productions that capture the mutual influence of coupled subsystems. This framework is applied to diverse setups which are usually studied separately in information thermodynamics. The third part covers the case of important system variables being hidden from observation. The problem is motivated by presenting a microswimmer model and showing that its movement can be approximated by active Brownian motion. However, its energy dissipation is massively underestimated by this procedure. It is shown that this is a consequence of the fact that the swimming mechanism is a hidden variable. Subsequently, different methods of effective description are discussed and applied to a simple model system with which the impact of hidden slow degrees of freedom on fluctuation theorems is studied. Finally, a setting is investigated in which it is possible to give bounds for the hidden entropy production from only partial observation of the system dynamics by fitting an underlying hidden Markov process to the observable data.