用在线贝叶斯学习让数字孪生自动更新状态预测，提升工程决策效率。
Adaptive digital twins for predictive decision-making: Online Bayesian learning of transition dynamics
- 通过动态贝叶斯网络建模物理与虚拟系统的双向交互
- 支持从状态到状态的转移概率实时更新，提升预测精度
- 适合需要持续优化维护策略的基础设施监测场景
本研究展示自适应能力如何提升数字孪生在土木工程中的价值。聚焦于通过概率图模型表示的数字孪生中状态转移模型的自适应性。物理与虚拟域间的双向交互采用动态贝叶斯网络建模。将状态转移概率视为带有共轭先验的随机变量，实现无需复杂计算的层次化在线学习。我们提供了比现有文献更广泛的分布类型数学框架。为实现精确动态策略更新，通过强化学习求解参数化马尔可夫决策过程。所提出的自适应数字孪生框架具有更强个性化、更高鲁棒性及更优成本效益。我们在铁路桥梁结构健康监测与维护规划案例中验证了该方法的有效性。
原文摘要 · Abstract (English)
This work shows how adaptivity can enhance value realization of digital twins in civil engineering. We focus on adapting the state transition models within digital twins represented through probabilistic graphical models. The bi-directional interaction between the physical and virtual domains is modeled using dynamic Bayesian networks. By treating state transition probabilities as random variables endowed with conjugate priors, we enable hierarchical online learning of transition dynamics from a state to another through effortless Bayesian updates. We provide the mathematical framework to account for a larger class of distributions with respect to the current literature on digital twins. To compute dynamic policies with precision updates we solve parametric Markov decision processes through reinforcement learning. The proposed adaptive digital twin framework enjoys enhanced personalization, increased robustness, and improved cost-effectiveness. We assess our approach on a case study involving structural health monitoring and maintenance planning of a railway bridge.
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