Automatic recognition of different 3d soliton wave types using deep learning methods

Abstract

In this study, deep learning (DL) techniques are used for automatic recognition of soliton wave types. Accurate characterization of soliton wave species has the potential to improve their precise and effective use in various fields such as optics, electronics, telecommunications. In addition, the accuracy of the results obtained in the equation solutions will be demonstrated due to the determined wave type. Therefore, soliton analyses were performed at the beginning of the study using equations such as Korteweg-de Vries and nonlinear Schr & ouml;dinger. These analyses led to the creation of 3D visual representations for eight distinct soliton types, including breather, kink, anti-kink, cusp, loop, lump, multi-peak, and rogue soliton. Following the generation of these images, we proceeded with a rigorous labeling process to prepare the data for the subsequent deep-learning phase. For this phase, we explored the performance of three prominent DL architectures: ResNet50V2, InceptionV3, and DenseNet169. Each architecture underwent separate training, validation, and testing procedures. Among these architectures, ResNet50V2 emerged as the top performer, consistently achieving high accuracies throughout the training, validation, and testing stages. Specifically, ResNet50V2 achieved training, validation, and testing accuracies of 0.9979, 1.00, and 1.00, respectively. Additionally, precision, recall, f1-score, weighted average, and macro average metrics all demonstrated perfect scores of 1.00. After completing the model training and evaluation process, we further assessed the model's performance by testing it on diverse 3D images, all of which resulted in predictions with 100% accuracy. Subsequently, we applied the ResNet50V2 architecture to test datasets representing six distinct soliton types documented in existing literature, successfully achieving accurate predictions for all instances. Through experiments conducted using both internally generated dataset pools and literature-derived images, the application of deep learning facilitated precise recognition of 3D soliton-type representations, underscoring its effectiveness in this domain.