  Data-driven modeling is an imperative tool in various industrial
applications, including many applications in the sectors of aeronautics and
commercial aviation. These models are in charge of providing key insights, such
as which parameters are important on a specific measured outcome or which
parameter values we should expect to observe given a set of input parameters.
At the same time, however, these models rely heavily on assumptions (e.g.,
stationarity) or are "black box" (e.g., deep neural networks), meaning that
they lack interpretability of their internal working and can be viewed only in
terms of their inputs and outputs. An interpretable alternative to the "black
box" models and with considerably less assumptions is symbolic regression (SR).
SR searches for the optimal model structure while simultaneously optimizing the
model's parameters without relying on an a-priori model structure. In this
work, we apply SR on real-life exhaust gas temperature (EGT) data, collected at
high frequencies through the entire flight, in order to uncover meaningful
algebraic relationships between the EGT and other measurable engine parameters.
The experimental results exhibit promising model accuracy, as well as
explainability returning an absolute difference of 3{\deg}C compared to the
ground truth and demonstrating consistency from an engineering perspective.
