Active Digital Twins via Active Inference

TL;DR

This paper proposes active digital twins based on active inference, enabling autonomous, adaptive, and resilient monitoring and prediction of physical systems through active information seeking and decision-making.

Contribution

It introduces a novel active digital twin framework using active inference, unifying inference, decision-making, and learning for dynamic system modeling.

Findings

Active digital twins outperform traditional passive models in exploration.

The framework enables bidirectional perception-action interaction.

Virtual experiments show improved autonomy and resilience.

Abstract

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.