Quantifying dissipation using fluctuating currents
Quantifying dissipation using fluctuating currents
复制标题
DOI：
10.1038/s41467-019-09631-x
复制
发表时间：
2019-04-10
影响因子：
16.6
通讯作者：
Fakhri, Nikta
中科院分区：
文献类型：
--
作者：
Li, Junang;Horowitz, Jordan M.;Fakhri, Nikta
耦合到多个热力学储存库的系统可以表现出非平衡动力学，打破详细的平衡来产生电流。为了给这些水流提供动力，水库的熵增加了。熵产生或耗散率是衡量非平衡过程统计不可逆性的一个指标。通过在几个生物系统中测量这种不可逆性，最近的实验检测到特定的系统不处于平衡状态。在这里，我们讨论了三种策略来用熵产生率的量化来取代二元分类(平衡与非平衡)。为了说明这一点，我们为一个分析上容易处理的钢珠弹簧模型的演化生成时间序列数据。概率流可以被推断并用来间接量化熵产生率，但这种方法需要高维系统中令人望而却步的数据量。这种维度灾难可以通过使用热力学不确定关系来限制使用概率流中的统计涨落来限制熵产生率来部分缓解。
Systems coupled to multiple thermodynamic reservoirs can exhibit nonequilibrium dynamics, breaking detailed balance to generate currents. To power these currents, the entropy of the reservoirs increases. The rate of entropy production, or dissipation, is a measure of the statistical irreversibility of the nonequilibrium process. By measuring this irreversibility in several biological systems, recent experiments have detected that particular systems are not in equilibrium. Here we discuss three strategies to replace binary classification (equilibrium versus nonequilibrium) with a quantification of the entropy production rate. To illustrate, we generate time-series data for the evolution of an analytically tractable bead-spring model. Probability currents can be inferred and utilized to indirectly quantify the entropy production rate, but this approach requires prohibitive amounts of data in high-dimensional systems. This curse of dimensionality can be partially mitigated by using the thermodynamic uncertainty relation to bound the entropy production rate using statistical fluctuations in the probability currents.