  This work develops a methodology for creating a data-driven digital twin from
a library of physics-based models representing various asset states. The
digital twin is updated using interpretable machine learning. Specifically, we
use optimal trees---a recently developed scalable machine learning method---to
train an interpretable data-driven classifier. Training data for the classifier
are generated offline using simulated scenarios solved by the library of
physics-based models. These data can be further augmented using experimental or
other historical data. In operation, the classifier uses observational data
from the asset to infer which physics-based models in the model library are the
best candidates for the updated digital twin. The approach is demonstrated
through the development of a structural digital twin for a 12ft wingspan
unmanned aerial vehicle. This digital twin is built from a library of
reduced-order models of the vehicle in a range of structural states. The
data-driven digital twin dynamically updates in response to structural damage
or degradation and enables the aircraft to replan a safe mission accordingly.
Within this context, we study the performance of the optimal tree classifiers
and demonstrate how their interpretability enables explainable structural
assessments from sparse sensor measurements, and also informs optimal sensor
placement.
