Applications of convolutional neural networks for spectral analysis

Starting from the basic mathematical principle of a convolutional neural network (CNN), this chapter introduces the unique advantages of CNNs in spectral analysis as well as data training strategy and sampling principles. Applications of spectral prediction and ultrafast signal analysis are first introduced. Then, generative models for reverse design are reviewed. Finally, from the perspective of feature extraction, we discuss the applications of an autoencoder in spectral feature extraction and assisting researchers in analysis. These data-driven CNN algorithms can achieve spectral analysis, prediction, and reverse optimization while also helping researchers focus on innovations rather than laborious processes. They are promising for future high-throughput applications.