abstract = "Modeling of dynamical systems is a necessary
preparatory step for many engineering applications,
such as controlling the roll, pitch and yaw of an
aircraft, assessing the structural integrity of a
bridge and load scheduling for management of
electricity grids. In each of these applications, a
dynamical model is derived or estimated for a
particular model-based method: designing model-based
control schemes, performing system analysis or making
predictions. A model of a dynamical system can be
derived from first principles, by applying physical
laws that govern the dynamics of the system. However,
as engineering systems become increasingly complex, a
first principles approach to modelling dynamical
systems becomes cumbersome and time-consuming. An
alternate approach to modelling of dynamical systems is
to estimate (a part of) the model from data inferred
from the dynamical system. More than five decades of
research has resulted in a variety of data-driven
modelling techniques. Most of these techniques require
an expert user to make some well-informed decisions and
assumptions. The quality of the identified model, and
consequently the performance of the modelbased method
in the corresponding application, may be significantly
influenced by these decisions. Hence, for inexperienced
users, obtaining the desired model quality with respect
to the use-case of the model can be a demanding task
with many pitfalls...",
notes = "Proefschrift. Also known as
\cite{00af7dc4abcc4a2db6662bb915178b40}