论文标题
带有反复神经网络的时间依赖性原子磁力测定法
Time-dependent atomic magnetometry with a recurrent neural network
论文作者
论文摘要
我们建议采用一个复发的神经网络来估计原子集合上连续光学旋转测量的波动磁场。我们表明，编码器架构神经网络可以处理测量数据，并在记录的信号和时间相关的磁场之间学习准确的映射。该方法的性能与卡尔曼过滤相当，而它没有限制其应用于特定测量和物理系统的理论假设。
We propose to employ a recurrent neural network to estimate a fluctuating magnetic field from continuous optical Faraday rotation measurement on an atomic ensemble. We show that an encoder-decoder architecture neural network can process measurement data and learn an accurate map between recorded signals and the time-dependent magnetic field. The performance of this method is comparable to Kalman filters while it is free of the theory assumptions that restrict their application to particular measurements and physical systems.
