What question did this study set out to answer?

This work aims to enhance the utility of digital twins by developing an adaptive Bayesian framework for model selection and optimization.

July 30, 2026Mechanical Systems and Signal ProcessingOpen Access

Utility-driven adaptive model selection for digital twinning

View Full Paper

Ask AI

Bookmark

Authors

VKVlachas KonstantinosETH Zurich AKAntonios KamariotisETH Zurich ECEleni ChatziETH Zurich

Discussion

Member takes

Implication

Randomized trial evaluates an adaptive Bayesian framework for optimizing digital twin models, suggesting improved decision support in engineering contexts.

Key Points

This work aims to enhance the utility of digital twins by developing an adaptive Bayesian framework for model selection and optimization.
Introduced an adaptive Bayesian framework for model fidelity and efficiency optimization.
Integrated reduced-order models within Bayesian inference for decision support.
Utilized real-time monitoring data for recursive model selection.
The framework identified the least costly model achieving the required predictive accuracy under uncertainty.
Achieved a balance between precision and computational efficiency for real-time decision-making.
Enabled automated decisions regarding model retention or retraining based on evolving system data, enhancing adaptability.

Cite This Study

Konstantinos et al. (2026) studied this question.

synapsesocial.com/papers/6a6af4ad60e2b924d3ea02e2 https://doi.org/10.1016/j.ymssp.2026.114717

View Full Paper

Ask AI

Bookmark

Implication

Randomized trial evaluates an adaptive Bayesian framework for optimizing digital twin models, suggesting improved decision support in engineering contexts.

Utility-driven adaptive model selection for digital twinning

Discussion

Member takes

Implication

Key Points

Cite This Study

Utility-driven adaptive model selection for digital twinning

Discussion

Member takes

Implication

Key Points

Cite This Study

Also Consider

Also Consider