论文标题
结合复杂系统中熵产生的下限与多个相互作用的组件
Combining lower bounds on entropy production in complex systems with multiple interacting components
论文作者
论文摘要
在过去的二十年中，统计物理学的一场革命，将其推广到任意大小的系统，而随意远离均衡。这些新结果中有许多是基于分析根据马尔可夫过程进化的系统熵的动力学。这些结果包括一个名为``随机热力学''的子场。传统上，随机热力学中一些最有力的结果与单一的整体系统有关，自身发展，忽略了这些系统的任何内部结构。在本章中，我回顾了由许多相互作用的组成系统组成的复杂系统中，可以实质上加强许多传统的随机热力学结果。这是通过``混合和匹配''来完成的那些传统结果，每个结果仅适用于相互作用系统的一个子集，从而在聚合，复杂的系统级别上产生更强大的结果。
The past two decades have seen a revolution in statistical physics, generalizing it to apply to systems of arbitrary size, evolving while arbitrarily far from equilibrium. Many of these new results are based on analyzing the dynamics of the entropy of a system that is evolving according to a Markov process. These results comprise a sub-field called ``stochastic thermodynamics''. Some of the most powerful results in stochastic thermodynamics were traditionally concerned with single, monolithic systems, evolving by themselves, ignoring any internal structure of those systems. In this chapter I review how in complex systems, composed of many interacting constituent systems, it is possible to substantially strengthen many of these traditional results of stochastic thermodynamics. This is done by ``mixing and matching'' those traditional results, to each apply to only a subset of the interacting systems, thereby producing a more powerful result at the level of the aggregate, complex system.
