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    This study presents a new hybrid method for self-evolving equipment digital twins (DTs), ensuring accurate real-time mirroring of physical assets. The approach uses data-physics integration with meta-learning and continual learning for adaptive model updates.

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    Area of Science:

    • Engineering
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Digital twins (DTs) require continuous updates to accurately reflect physical asset behavior.
    • Existing methods struggle with autonomous adaptation to dynamic real-world changes.
    • Bridging the gap between physics-based models and real-time data is challenging.

    Purpose of the Study:

    • To introduce a novel hybrid method for self-evolving equipment digital twins.
    • To enable continuous and accurate mirroring of physical counterparts by DTs.
    • To enhance the adaptability and real-time accuracy of digital twins.

    Main Methods:

    • A data-physics driven approach integrating meta-learning and continual learning.
    • Utilizing a Koopman autoencoder (KAE) for an extended residual model.
    • Employing the Reptile meta-learning algorithm for offline foundation model training.
    • Implementing an event-triggered mechanism for online continual learning updates.

    Main Results:

    • The proposed method enables digital twins to autonomously update models using real-time sensor data.
    • The hybrid approach ensures rapid adaptation to new, unseen scenarios.
    • Validation through a robot simulation case study demonstrates improved effectiveness and performance.

    Conclusions:

    • The fusion of offline meta-learning and online continual learning facilitates agile digital twin evolution.
    • The framework ensures digital twins accurately reflect the physical equipment's state in real-time.
    • This approach significantly enhances the self-evolution capabilities of equipment digital twins.