  Mathematical models are vital to the field of metrology, playing a key role
in the derivation of measurement results and the calculation of uncertainties
from measurement data, informed by an understanding of the measurement process.
These models generally represent the correlation between the quantity being
measured and all other pertinent quantities. Such relationships are used to
construct measurement systems that can interpret measurement data to generate
conclusions and predictions about the measurement system itself. Classic models
are typically analytical, built on fundamental physical principles. However,
the rise of digital technology, expansive sensor networks, and high-performance
computing hardware have led to a growing shift towards data-driven
methodologies. This trend is especially prominent when dealing with large,
intricate networked sensor systems in situations where there is limited expert
understanding of the frequently changing real-world contexts. Here, we
demonstrate the variety of opportunities that data-driven modeling presents,
and how they have been already implemented in various real-world applications.
