Enhancing Digital Twin Technology with Reinforcement Learning and Neural Network-Based Predictive Analytics

Abstract

This research paper explores the integration of reinforcement learning (RL) and neural network-based predictive analytics to enhance the capabilities of digital twin technology. Digital twins, virtual replicas of physical systems, are becoming increasingly crucial for real-time monitoring, control, and optimization across various industries. However, there is a growing demand for improving their adaptability and predictive accuracy. This study proposes a novel framework that incorporates reinforcement learning algorithms to enable digital twins to autonomously adapt to dynamic environments, optimizing their responses and decision-making capabilities. Additionally, neural networks are utilized to enhance predictive analytics, offering more accurate and timely forecasts of system behaviors and potential failures. A series of experiments were conducted across multiple domains, including manufacturing and smart city infrastructures, demonstrating the enhanced performance of digital twins with the proposed integration. Results indicate significant improvements in predictive accuracy, adaptability, and overall operational efficiency, affirming the potential of reinforcement learning and neural network synergies in advancing digital twin technology. This paper concludes by discussing the implications of these findings for future research and applications, offering a pathway for further innovations in digital twin systems.