论文标题
100激光束阵列的实验相控制，并在误差循环中对神经网络进行准强化学习
Experimental phase control of a 100 laser beam array with quasi-reinforcement learning of a neural network in an error reduction loop
论文作者
论文摘要
提出了一种创新的方案，用于在二维激光束阵列中对相位的动态控制。它基于一个简单的神经网络，该神经网络可以通过诱导的散射图案的强度通过由扩散器制成的相强度变压器来预测复杂场阵列。迭代的相校正通过反馈环将相位调节器应用于激光场数组阵列，以将数组设置为规定的相值。一个关键特征是将一种强化学习方法用于神经网络培训，该方法考虑了迭代的校正。对概念验证系统的实验证明了该方案的高性能和可扩展性，其阵列最多为100个激光束和波长1/30的相位设置。
An innovative scheme is proposed for the dynamic control of phase in two-dimensional laser beam array. It is based on a simple neural network that predicts the complex field array from the intensity of the induced scattered pattern through a phase intensity transformer made of a diffuser. Iterated phase corrections are applied on the laser field array by phase modulators via a feedback loop to set the array to prescribed phase values. A crucial feature is the use of a kind of reinforcement learning approach for the neural network training which takes account of the iterated corrections. Experiments on a proof of concept system demonstrated the high performance and scalability of the scheme with an array of up to 100 laser beams and a phase setting at 1/30 of the wavelength.
