Published August 26, 2025
| Version v1
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A Roadmap for Iterative Calibration of Digital Twins via Adaptive Observers
Authors/Creators
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1.
University of Idaho
Contributors
Researcher (2):
Supervisor:
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1.
University of Idaho
Description
While iterative calibration of computational models is a fundamental aspect of digital twins, it has been largely overlooked. Instead of focusing on parameter identification for static models, the implementation of digital twins requires not only high-resolution computational models, but also the ability to assimilate patient-specific data continuously.
Here, we envisage a roadmap for adaptive observers algorithms to address this challenge. By leveraging computational models and patient-specific measurements, adaptive observers enable the estimation of unmeasurable states while continuously adapting model parameters.
Integrating adaptive observers into digital twins offers a paradigm shift: transforming them from static representations into living, evolving systems that advance personalized medicine.
Files
AdapObs_DT_MiniRev.pdf
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