Learn to Update Digital Twins with Incremental Scenarios

Mengjie Lee, Yujiao Hu, Yining Zhu, Xiaomao Zhou, Yue Zhao, Xingshe Zhou

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publicationNetAISys 2024 - Proceedings of the 2024 2nd International Workshop on Networked AI Systems
PublisherAssociation for Computing Machinery, Inc
Pages7-12
Number of pages6
ISBN (Electronic)9798400706615
DOIs
StatePublished - 3 Jun 2024
Event2nd International Workshop on Networked AI Systems, NetAISys 2024 - Minato-ku, Japan
Duration: 3 Jun 20247 Jun 2024

Publication series

NameNetAISys 2024 - Proceedings of the 2024 2nd International Workshop on Networked AI Systems

Conference

Conference2nd International Workshop on Networked AI Systems, NetAISys 2024
Country/TerritoryJapan
CityMinato-ku
Period3/06/247/06/24

Keywords

  • Dataset construction
  • Digital twin
  • Incremental learning
  • Intelligent model updating
  • Scenario-sensitive updating

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