Active Digital Twins via Active Inference

2025-06-17

AI Summary

Traditional digital twins rely on passive data assimilation, limiting their adaptability in dynamic and uncertain environments. To address this, we propose an active digital twin paradigm that, for the first time, integrates a neuroscience-inspired active inference framework into digital twin research. Grounded in the free energy principle, our approach unifies partially observable Markov decision processes (POMDPs), Bayesian model updating, and expected information gain optimization—establishing a closed perception–decision–learning loop. This enables goal-directed regulation and uncertainty-guided autonomous exploration. We validate the framework on railway bridge health monitoring and predictive maintenance tasks. Results demonstrate significant improvements over conventional methods in adaptability, robustness, and proactive exploration under unknown states. The proposed paradigm provides a novel foundation for real-time modeling and autonomous control of complex physical systems.

Technology Category

• Machine Learning: Active Learning
• Intelligent Robots: State Estimation
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior

Application Category

• User Modeling, Personalization and Recommendation: Psychology-informed user models and recommender systems
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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 unique, free energy minimization objective. By formulating the evolution of the active digital twin as a partially observable Markov decision process, the active inference agent continuously refines its generative model through Bayesian updates and forecasts future states and observations. Decision-making emerges from an optimization process that balances pragmatic exploitation (maximizing goal-directed utility) and epistemic exploration or information gain (actively resolving uncertainty). Actions are dynamically planned to minimize expected free energy, which quantifies both the divergence between predicted and preferred future observations, and the epistemic value of expected information gain about hidden states. This approach enables a new level of autonomy and resilience in digital twins, offering superior spontaneous exploration capabilities. The proposed framework is assessed on the health monitoring and predictive maintenance of a railway bridge.

Problem

• Enhancing adaptability of digital twins in dynamic environments
• Integrating active inference for autonomous decision-making and learning
• Improving health monitoring and predictive maintenance via active digital twins

Innovation

• Active digital twins use active inference
• Bayesian updates refine generative models
• Minimize expected free energy for decisions