A digital twin contains up-to-date data-driven models of the physical world being studied and can use simulation to optimise the physical world. However, the analysis made by the digital twin is valid and reliable only when the model is equivalent to the physical world. Maintaining such an equivalent model is challenging, especially when the physical systems being modelled are intelligent and autonomous. The paper focuses in particular on digital twin models of intelligent systems where the systems are knowledge-aware but with limited capability. The digital twin improves the acting of the physical system at a meta-level by accumulating more knowledge in the simulated environment. The modelling of such an intelligent physical system requires replicating the knowledge-awareness capability in the virtual space. Novel equivalence maintaining techniques are needed, especially in synchronising the knowledge between the model and the physical system. This paper proposes the notion of knowledge equivalence and an equivalence maintaining approach by knowledge comparison and updates. A quantitative analysis of the proposed approach confirms that compared to state equivalence, knowledge equivalence maintenance can tolerate deviation thus reducing unnecessary updates and achieve more Pareto efficient solutions for the trade-off between update overhead and simulation reliability.

翻译：数字孪生包含被研究物理世界的最新数据驱动模型，并可通过仿真优化物理世界。然而，只有当模型与物理世界等价时，数字孪生所做的分析才有效且可靠。维持这种等价模型极具挑战性，尤其在所建模的物理系统具备智能性和自主性时。本文特别聚焦于智能系统的数字孪生模型，此类系统具有知识感知能力但能力有限。数字孪生通过在仿真环境中积累更多知识，在元层面改善物理系统的行为。对此类智能物理系统的建模需要在虚拟空间中复现知识感知能力。需要创新的等价性维持技术，尤其是在模型与物理系统之间的知识同步方面。本文提出知识等价性概念，以及通过知识比较与更新来维持等价性的方法。对所提方法的定量分析证实：相较于状态等价性，维持知识等价性可容忍偏差，从而减少不必要的更新，并在更新开销与仿真可靠性之间实现更帕累托高效的解决方案。