  Purpose: Simulation-based digital twins represent an effort to provide
high-accuracy real-time insights into operational physical processes. However,
the computation time of many multi-physical simulation models is far from
real-time. It might even exceed sensible time frames to produce sufficient data
for training data-driven reduced-order models. This study presents TwinLab, a
framework for data-efficient, yet accurate training of neural-ODE type
reduced-order models with only two data sets. Design/methodology/approach:
Correlations between test errors of reduced-order models and distinct features
of corresponding training data are investigated. Having found the single best
data sets for training, a second data set is sought with the help of similarity
and error measures to enrich the training process effectively. Findings: Adding
a suitable second training data set in the training process reduces the test
error by up to 49% compared to the best base reduced-order model trained only
with one data set. Such a second training data set should at least yield a good
reduced-order model on its own and exhibit higher levels of dissimilarity to
the base training data set regarding the respective excitation signal.
Moreover, the base reduced-order model should have elevated test errors on the
second data set. The relative error of the time series ranges from 0.18% to
0.49%. Prediction speed-ups of up to a factor of 36,000 are observed.
Originality: The proposed computational framework facilitates the automated,
data-efficient extraction of non-intrusive reduced-order models for digital
twins from existing simulation models, independent of the simulation software.
