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.

翻译：我们采用蒙特卡洛方法和遗传算法，为模拟的涨落纳米系统训练基于神经网络的反馈控制协议。这些协议将反馈过程获得的信息转化为热量或功，从而能够从光学陷阱捕获的胶体粒子中提取功，以及从经历磁化反转的伊辛模型中吸收熵。该学习框架无需系统先验知识，仅依赖于实验可测量的数据，并能够扩展至具有相当复杂度的系统。它可在实验室中用于学习将测量信息转化为储存功或热量的涨落纳米系统协议。