Digital Twinning

Digital twinning creates virtual replicas of physical systems, aiming to improve monitoring, prediction, and control. Current research emphasizes applications in diverse fields, including manufacturing, infrastructure monitoring, and autonomous systems, employing techniques like machine learning (e.g., convolutional neural networks, federated learning), multi-agent systems, and advanced simulation frameworks (e.g., ray tracing) to enhance model accuracy and efficiency. This technology's impact spans improved operational efficiency, predictive maintenance, and accelerated design processes across various industries, ultimately leading to more robust and adaptable systems.