  System identification aims to build models of dynamical systems from data.
Traditionally, choosing the model requires the designer to balance between two
goals of conflicting nature; the model must be rich enough to capture the
system dynamics, but not so flexible that it learns spurious random effects
from the dataset. It is typically observed that model validation performance
follows a U-shaped curve as the model complexity increases. Recent developments
in machine learning and statistics, however, have observed situations where a
"double-descent" curve subsumes this U-shaped model-performance curve. With a
second decrease in performance occurring beyond the point where the model has
reached the capacity of interpolating - i.e., (near) perfectly fitting - the
training data. To the best of our knowledge, however, such phenomena have not
been studied within the context of the identification of dynamic systems. The
present paper aims to answer the question: "Can such a phenomenon also be
observed when estimating parameters of dynamic systems?" We show the answer is
yes, verifying such behavior experimentally both for artificially generated and
real-world datasets.
