Digital Twins and Simulation Using Generative Models for Defense Technology

Abstract

In today's defense systems, making fast decisions, keeping equipment in good condition, and being ready for operations are really important. This paper presents a new method for creating Digital Twins for defense equipment and systems using simulation models based on Generative AI. Digital Twins are like virtual copies of real-world assets such as unmanned vehicles, drones, and weapons. These virtual copies are constantly updated with real-time data from sensors. By using Generative Models like GANs and diffusion networks, the system can create various battlefield scenarios, predict when parts might fail, and test mission plans without any real-world risks. This AI-based simulation helps with predicting maintenance needs, reduces the cost of testing, and supports mission planning through realistic training environments. The research shows how combining Digital Twin technology with Generative AI can greatly improve defense preparedness, reliability, and ability to handle challenges. The goal is to provide defense organizations with better, faster, and more data-driven decision-making tools for future military system.