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

arXiv: https://arxiv.org/abs/2512.13919 · HTML: https://mlsanity.com/paper/2512.13919/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 96 references that are in the map.
- First posted on arXiv 15 Dec 2025.

## Builds on (most cited first, 18 of 18)

- [A Survey of Reinforcement Learning Algorithms for Dynamically Varying Environments](https://mlsanity.com/paper/2005.10619/) (2020)
- [A Probabilistic Graphical Model Foundation for Enabling Predictive Digital Twins at Scale](https://mlsanity.com/paper/2012.05841/) (2020)
- [A Review of Vibration-Based Damage Detection in Civil Structures: From Traditional Methods to Machine Learning and Deep Learning Applications](https://mlsanity.com/paper/2004.04373/) (2020)
- [Managing engineering systems with large state and action spaces through deep reinforcement learning](https://mlsanity.com/paper/1811.02052/) (2018)
- [A Comprehensive Review of Digital Twin -- Part 1: Modeling and Twinning Enabling Technologies](https://mlsanity.com/paper/2208.14197/) (2022)
- [POMDP inference and robust solution via deep reinforcement learning: An application to railway optimal maintenance](https://mlsanity.com/paper/2307.08082/) (2023)
- [Predictive Digital Twin for Optimizing Patient-Specific Radiotherapy Regimens under Uncertainty in High-Grade Gliomas](https://mlsanity.com/paper/2308.12429/) (2023)
- [Bridging POMDPs and Bayesian decision making for robust maintenance planning under model uncertainty: An application to railway systems](https://mlsanity.com/paper/2212.07933/) (2022)
- [A digital twin framework for civil engineering structures](https://mlsanity.com/paper/2308.01445/) (2023)
- [Online structural health monitoring by model order reduction and deep learning algorithms](https://mlsanity.com/paper/2103.14328/) (2021)
- [InVAErt networks: a data-driven framework for model synthesis and identifiability analysis](https://mlsanity.com/paper/2307.12586/) (2023)
- [On the latent dimension of deep autoencoders for reduced order modeling of PDEs parametrized by random fields](https://mlsanity.com/paper/2310.12095/) (2023)
- [Active Digital Twins via Active Inference](https://mlsanity.com/paper/2506.14453/) (2025)
- [High-Fidelity Digital Twins: Detecting and Localizing Weaknesses in Structures](https://mlsanity.com/paper/2311.10925/) (2023)
- [Model bias identification for Bayesian calibration of stochastic digital twins of bridges](https://mlsanity.com/paper/2312.00664/) (2023)
- [Enhancing Bayesian model updating in structural health monitoring via learnable mappings](https://mlsanity.com/paper/2405.13648/) (2024)
- [Mastering truss structure optimization with tree search](https://mlsanity.com/paper/2406.06145/) (2024)
- [A Reduced Order Model conditioned on monitoring features for estimation and uncertainty quantification in engineered systems](https://mlsanity.com/paper/2407.17139/) (2024)

## Abstract (arXiv)

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.