论文标题
由神经网络重建的四波混合的相空间拓扑
Phase space topology of four-wave mixing reconstructed by a neural network
论文作者
论文摘要
通过利用实验测量与监督机器学习策略的组合，重建了光纤中理想四波混合的动力学。训练数据由功率依赖的光谱阶段和振幅组成，记录在短片纤维的输出下。神经网络能够准确预测数十公里上的非线性动力学，并检索相位空间拓扑的主要特征，包括多个Fermi-Pasta-Pasta-ulam复发周期和系统分离质边界。
The dynamics of ideal four-wave mixing in optical fiber is reconstructed by taking advantage of the combination of experimental measurements with supervised machine learning strategies. The training data consist of power-dependent spectral phase and amplitude recorded at the output of a short segment of fiber. The neural network is able to accurately predict the nonlinear dynamics over tens of kilometers, and to retrieve the main features of the phase space topology including multiple Fermi-Pasta-Ulam recurrence cycles and the system separatrix boundary.
