Analysis of rogue wave in the mid-infrared supercontinuum under femtosecond weak seed pulse conditions based on deep learning

Abstract

The generation process of rogue wave (RW) is affected by noise, which is an unstable state, and the existence of RW will reduce the stability of mid-infrared supercontinuum. However, the process of studying RW requires a large amount of data simulation and statistics, and traditional methods are time-consuming and inefficient. Therefore, this paper adopts long short-term memory (LSTM) neural network to obtain the spectrum information after transmission for a certain distance according to the waveform information of the incident pulse. The results show that the LSTM neural network structure can train and predict the peak power, time deviation information, time intensity evolution and spectrum evolution of RW after 10 cm propagation with only changing the number of internal units. And it performs well on both large and small data sets.