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：我们利用蒙特卡洛和遗传算法为模拟波动纳米系统培训神经-网络反馈控制协议,这些协议将反馈过程获得的信息转换成热或工作,允许从光学陷阱所拉取的共聚颗粒提取工作,并允许Ising模型吸收正发生磁化逆转的共聚物。学习框架不要求事先了解该系统,仅取决于实验中可获取的测量数据,以及相当复杂的系统的规模。它可以在实验室中用于学习将测量信息转换成存储的工作或热的波动纳米系统的协议。