论文标题
机器中的恶魔：学习从波动的纳米系统中提取工作并吸收熵
Demon in the machine: learning to extract work and absorb entropy from fluctuating nanosystems
论文作者
论文摘要
我们使用蒙特卡洛和遗传算法来训练模拟波动纳米系统的神经网络反馈控制方案。这些协议将通过反馈过程获得的信息转换为热量或工作，从而从光学陷阱中提取的胶体粒子中提取工作，并通过磁化逆转的ISING模型吸收熵。学习框架不需要对系统的先验知识，仅取决于可在实验上可访问的测量，并缩放到相当复杂的系统。它可以在实验室中用于学习波动的纳米系统方案，以将测量信息转换为存储的工作或热量。
We use Monte Carlo and genetic algorithms to train neural-network feedback-control protocols for simulated fluctuating nanosystems. These protocols convert the information obtained by the feedback process into heat or work, allowing the extraction of work from a colloidal particle pulled by an optical trap and the absorption of entropy by an Ising model undergoing magnetization reversal. The learning framework requires no prior knowledge of the system, depends only upon measurements that are accessible experimentally, and scales to systems of considerable complexity. It could be used in the laboratory to learn protocols for fluctuating nanosystems that convert measurement information into stored work or heat.
