Learn to Update Digital Twins with Incremental Scenarios

Abstract

Digital twins serve as vital tools for monitoring and simulating real-world systems, yet ensuring their accuracy and adaptability in dynamic scenarios remains a challenge. In this paper, we introduce FlexiTwin, a digital twin updating assistance platform seamlessly integrated with AdaSor, a lightweight adaptive data selector. FlexiTwin automates the construction of incremental learning datasets for updating digital twins within specified time constraints, ensuring their adaptation to new scenarios while preserving historical knowledge. Through simulations focusing on UAV energy management, we show that FlexiTwin substantially enhances the adaptability of digital twins to new scenarios while effectively preserving their accuracy in historical scenarios.