Self-Evolution of Hybrid Data-Physics Equipment Digital Twin Using Meta Learning and Continual Learning

This article introduces a novel hybrid method to enable the self-evolution of equipment digital twins (DTs), allowing them to continuously and accurately mirror their physical counterparts. Self-evolution is the process by which a DT autonomously updates its models using real-time sensor data, adapting to dynamic real-world behavior. To enhance this process, we propose a data-physics driven approach that synergistically integrates meta-learning and continual learning. Our method begins by designing an extended residual model using a Koopman autoencoder (KAE) neural network. This component bridges the gap between an imperfect analytical physics model and actual equipment behavior. Next, we employ the Reptile meta-learning algorithm to train offline a versatile foundation model on historical data, endowing it with strong adaptability for rapid learning from new information. A key innovation is a periodic event-triggered mechanism, which monitors the DT's simulation accuracy against a fixed time window. When a performance discrepancy is detected, it automatically triggers a self-evolution cycle. The foundation model is then updated through a fine-tuning strategy based on continual learning with random reinitialization. This fusion of offline meta-learning and online continual learning allows the DT to quickly adapt to new, unseen scenarios, ensuring it reflects the physical equipment's state in real-time. We validate the effectiveness and improved performance of our proposed framework through a comprehensive robot simulation case study.