Network Simulation

Network simulation aims to model the behavior of complex networks, from communication systems to intelligent warehouses, enabling researchers to test and optimize designs without physical implementation. Current research emphasizes integrating machine learning models, such as convolutional neural networks (CNNs), generative adversarial networks (GANs), graph neural networks (GNNs), and recurrent neural networks (RNNs), into simulators to improve accuracy, efficiency, and the ability to handle large-scale and dynamic systems. This allows for more realistic modeling of diverse scenarios, including anomaly detection, resource allocation, and performance prediction, ultimately leading to improved network design and management in various applications.