Adaptive digital twins for predictive decision-making: Online Bayesian learning of transition dynamics

Researchers have developed a method for civil engineering digital twins to update their state transition models in real time using online Bayesian learning. This approach allows infrastructure simulations to adjust to changing environmental conditions or structural wear automatically. By incorporating these dynamic updates, the technology enables more accurate predictive decision-making for long-term maintenance and risk management in physical projects.