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Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

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A probabilistic graphical model foundation for enabling predictive digital twins at scale.

Michael G Kapteyn¹, Jacob V R Pretorius², Karen E Willcox³

¹Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA, USA.

Nature Computational Science

|January 13, 2024

Summary

This summary is machine-generated.

This study introduces a unified mathematical framework using probabilistic graphical models for scalable digital twins. This approach enables robust, principled calibration and updating for diverse applications, including unmanned aerial vehicles.

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Area of Science:

Engineering
Computer Science
Mathematics

Background:

Digital twins require scalable, unified mathematical formulations beyond custom implementations.
Current methods lack a robust framework for large-scale digital twin deployment.

Purpose of the Study:

To propose a probabilistic graphical model as a formal mathematical representation for digital twins.
To enable scalable, robust, and principled digital twin implementations.

Main Methods:

Abstracting the asset-twin system as coupled dynamical systems.
Utilizing probabilistic graphical models for a formal mathematical definition.
Leveraging Bayesian statistics, dynamical systems, and control theory.

Main Results:

Demonstrated instantiation of a structural digital twin for an unmanned aerial vehicle (UAV).
Successfully calibrated the digital twin using experimental UAV data.
Illustrated dynamic updating and decision-making with in-flight damage simulation.

Conclusions:

The probabilistic graphical model provides a unified and scalable foundation for digital twin calibration and updating.
The proposed model is rigorous, flexible, and applicable across diverse domains.
This framework supports the management of entire fleets of digital twins.