Spatiotemporal nonlinear interactions in multimode fibers are of interest for beam shaping and frequency conversion by exploiting the nonlinear interaction of different pump modes from quasi-continuous wave to ultrashort pulses centered around visible to infrared pump wavelengths. The nonlinear effects in multi-mode fibers depend strongly on the excitation condition; however, relatively little work has been reported on this subject. Here, we present a machine learning approach to learn and control nonlinear frequency conversion inside multimode fibers. We experimentally show that the spectrum of the light at the output of the fiber can be tailored by a trained deep neural network. The network was trained with experimental data to learn the relation between the input spatial beam profile of the pump pulse and the spectrum of the light at the output of the multimode fiber. For a user-defined target spectrum, the network computes the spatial beam profile to be applied at the input of the fiber. The physical processes involved in the creation of new optical frequencies are cascaded stimulated Raman scattering as well as supercontinuum generation. We show experimentally that these processes are very sensitive to the spatial shape of the excitation and that a deep neural network is able to learn the relation between the spatial excitation at the input and the spectrum at its output. The method is limited to spectral shapes within the achievable nonlinear effects supported by the test setup, but the demonstrated method can be implemented to learn and control other spatiotemporal nonlinear effects.

中文翻译：

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用深度神经网络控制多模光纤的时空非线性

通过利用从准连续波到以可见到红外泵浦波长为中心的超短脉冲的不同泵浦模式的非线性相互作用，多模光纤中的时空非线性相互作用对于波束成形和频率转换非常重要。多模光纤中的非线性效应在很大程度上取决于激发条件。但是，关于该主题的报道很少。在这里，我们提出了一种机器学习方法来学习和控制多模光纤内部的非线性频率转换。我们实验表明，可以通过训练有素的深度神经网络来定制光纤输出处的光谱。用实验数据对网络进行了训练，以了解泵浦脉冲的输入空间光束轮廓与多模光纤输出处的光谱之间的关系。对于用户定义的目标光谱，网络计算要在光纤输入端应用的空间光束轮廓。创建新的光学频率所涉及的物理过程是级联的受激拉曼散射以及超连续谱的产生。我们通过实验表明，这些过程对激发的空间形状非常敏感，并且深度神经网络能够了解输入处的空间激发与输出处的光谱之间的关系。该方法仅限于测试装置支持的可达到的非线性效应内的光谱形状，