AI-Powered Digital Twins for Predictive Modeling in Smart Computing Systems

Abstract

The rapid advancement of smart computing systems necessitates innovative solutions for real-time monitoring, predictive maintenance, and system optimization. This paper explores the integration of AI-powered Digital Twins (DTs) to enhance predictive modeling in smart computing environments. By leveraging machine learning (ML), deep learning (DL), and realtime sensor data, AI-powered DTs create dynamic, virtual replicas of physical systems, enabling proactive decision-making, fault detection, and performance optimization. The proposed framework enhances system efficiency, resource utilization, and failure prediction accuracy through advanced AI techniques such as reinforcement learning, generative adversarial networks (GANs), and federated learning. Experimental results demonstrate that AI-driven DTs improve predictive accuracy by up to 40% compared to conventional models while reducing operational downtime and computational costs. The findings highlight the transformative potential of AIpowered Digital Twins in smart grids, healthcare, industrial automation, and intelligent transportation systems. Future research should focus on scalability, cybersecurity challenges, and interoperability to ensure seamless integration across diverse smart computing infrastructures.