Study of nonlinear optical diffraction patterns using machine learning models based on ResNet 152 architecture

Abstract

As the advancements in the field of artificial intelligence and nonlinear optics continues, new methods can be used to better describe and determine nonlinear optical phenomena. In this research, we aimed to analyze the diffraction patterns of an organic material and determine the nonlinear optical phase change and nonlinear refraction index of the material in question by utilizing the ResNet 152 convolutional neural network architecture in the regions of laser intensity that the diffraction rings are not clearly distinguishable. This approach can open new sights for optical material characterization in situations where the conventional methods do not apply.