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arXiv 2407.18542cond-mat.stat-mech
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主动粒子的最优闭环控制与最小信息引擎

Optimal closed-loop control of active particles and a minimal information engine

Rosalba Garcia-Millan, Janik Schüttler, Michael E. Cates, Sarah A. M. Loos

AI总结:

研究通过陷阱移动主动粒子的最优控制,发现闭环协议在有限持续时间内最小化平均功,并构建了精度和信息效率更高的周期性主动信息引擎。

摘要:

我们研究了通过陷阱以最小功输入移动主动粒子的基本问题。我们解析地证明,(开环)最优协议不受活性影响,但功涨落总是增加。对于依赖自推进初始测量的闭环协议,平均功在有限持续时间内存在最小值。利用这些见解,我们推导出一个最优周期性主动信息引擎,发现当使用游走-翻滚粒子而非主动奥恩斯坦-乌伦贝克粒子运行时,其具有更高的精度和信息效率,并且我们认为,其优于任何其他类型的主动粒子。

英文摘要:

We study the elementary problem of moving an active particle by a trap with minimum work input. We show analytically that (open-loop) optimal protocols are not affected by activity, but work fluctuations are always increased. For closed-loop protocols, which rely on initial measurements of the self-propulsion, the average work has a minimum for a finite persistence time. Using these insights, we derive an optimal periodic active information engine, which is found to have higher precision and information efficiency when operated with a run-and-tumble particle than for an active Ornstein-Uhlenbeck particle and, we argue, than for any other type of active particle.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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