Digital twins are transforming engineering and applied sciences by enabling real-time monitoring, simulation, and predictive analysis of physical systems and processes. However, conventional digital twins rely primarily on passive data assimilation, which limits their adaptability in uncertain and dynamic environments. This paper introduces the active digital twin paradigm, based on active inference. Active inference is a neuroscience-inspired Bayesian framework for probabilistic reasoning and predictive modeling that unifies inference, decision-making, and learning under a single free energy minimization objective. By modeling the dynamics of the coupled physical--digital system as a partially observable Markov decision process, active digital twins autonomously balance pragmatic exploitation (maximizing goal-directed utility) and epistemic exploration (actively resolving uncertainty). As action becomes an integral part of the inference process, active digital twins actively seek information to maintain synchronization with, and learn from their physical counterparts. The proposed framework is assessed through virtual experiments of structural health monitoring and predictive maintenance of a railway bridge. The application showcases the step-by-step construction of a generative model enabling bidirectional perception--action interaction. The results demonstrate that active digital twins exhibit superior exploration capabilities compared to traditional reactive approaches, enabling enhanced autonomy and resilience.

翻译：数字孪生技术通过实现物理系统与过程的实时监控、仿真及预测分析，正在推动工程与应用科学领域的变革。然而传统数字孪生主要依赖被动数据同化，在不确定动态环境中适应性受限。本文提出基于主动推理的主动数字孪生范式。主动推理是一种受神经科学启发的贝叶斯框架，通过单一自由能最小化目标统一推理、决策与学习过程，实现概率推理与预测建模。通过将耦合的物理-数字系统动态建模为部分可观测马尔可夫决策过程，主动数字孪生能够自主平衡实用开发（最大化目标导向效用）与认知探索（主动消解不确定性）。由于行动已成为推理过程的有机组成部分，主动数字孪生会主动搜寻信息以维持与物理实体的同步并从中学习。通过铁路桥梁结构健康监测与预测维护的虚拟实验评估该框架，展示了生成式模型的逐步构建过程及其实现双向感知-行动交互的机制。结果表明，与传统反应式方法相比，主动数字孪生具有更优的探索能力，可实现更强的自主性与鲁棒性。