In this work we present a general machine learning based scheme to optimize experimental control. The method utilizes the neural network to learn the relation between the control parameters and the control goal, with which the optimal control parameters can be obtained. The main challenge of this approach is that the labeled data obtained from experiments are not abundant. The central idea of our scheme is to use the active learning to overcome this difficulty. As a demonstration example, we apply our method to control evaporative cooling experiments in cold atoms. We have first tested our method with simulated data and then applied our method to real experiments. We demonstrate that our method can successfully reach the best performance within hundreds of experimental runs. Our method does not require knowledge of the experimental system as a prior and is universal for experimental control in different systems.

中文翻译：

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优化实验控制的主动学习方法

在这项工作中，我们提出了一个通用的基于机器学习的方案来优化实验控制。该方法利用神经网络学习控制参数与控制目标之间的关系，得到最优控制参数。这种方法的主要挑战是从实验中获得的标记数据并不丰富。我们方案的中心思想是使用主动学习来克服这个困难。作为演示示例，我们将我们的方法应用于控制冷原子中的蒸发冷却实验。我们首先用模拟数据测试了我们的方法，然后将我们的方法应用于实际实验。我们证明我们的方法可以在数百次实验运行中成功达到最佳性能。