With its applications in science and engineering, supercontinuum (SC) generation is a phenomenon widely studied in nonlinear fiber optics. The SC spectral properties are not difficult to measure, except those related to time. Fortunately, machine learning can help predict the time behavior of various nonlinear optics phenomena using spectral characteristics. In this study, supervised machine learning tools are used to evaluate the prediction accuracy of the soliton properties in a noisy environment. A neural network (NN) and a convolutional neural network (CNN) are implemented to assess the performance of these techniques in relation to predicting soliton properties when noise is included in a laser that pumps a nonlinear fiber optics. We conclude that the CNN shows better performance compared with NN, as it involves more data with the same quantity of simulations conducted in both cases, whereas NN can better predict the target in the absence of noise.

中文翻译：

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评估神经网络和卷积神经网络以预测量子噪声环境中的孤子特性

超连续谱（SC）的产生及其在科学和工程中的应用是非线性光纤中广泛研究的一种现象。除了那些与时间有关的特性外，SC光谱特性并不难测量。幸运的是，机器学习可以使用光谱特性帮助预测各种非线性光学现象的时间行为。在这项研究中，使用监督的机器学习工具来评估嘈杂环境中孤子特性的预测准确性。当泵浦非线性光纤的激光器中包含噪声时，将实现神经网络（NN）和卷积神经网络（CNN）来评估这些技术与预测孤子特性有关的性能。我们得出的结论是，与NN相比，CNN表现出更好的性能，