A Weighted Loss Function to Predict Control Parameters for Supercontinuum Generation Via Neural Networks

2021 IEEE 31st International Workshop on Machine Learning for Signal Processing (MLSP)(2021)

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摘要
Supercontinuum light is generated by a train of laser pulses propagating in an optical fiber. The parameters characterizing these pulses influence the spectrum of the light as it exits the fiber. While spectrum generation is a direct process governed by nonlinear equations that can be reproduced through numerical simulation, determining the parameters of the pulse generating a given spectrum is a ...
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关键词
Training,Supercontinuum generation,Inverse problems,Neural networks,Signal processing,Predictive models,Laser excitation
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