  As some of the most compact stellar objects in the universe, neutron stars
are unique cosmic laboratories. The study of neutron stars provides an ideal
theoretical testbed for investigating both physics at supra-nuclear densities
as well as fundamental physics. Their global astrophysical properties however
depend strongly on the star's internal structure, which is currently unknown
due to uncertainties in the equation of state. In recent years, a lot of work
has revealed the existence of universal relations between stellar quantities
that are insensitive to the equation of state. At the same time, the fields of
multimessenger astronomy and machine learning have both advanced significantly.
As such, there has been a confluence of research into their combination and the
field is growing. In this paper, we develop universal relations for rapidly
rotating neutron stars, by using supervised machine learning methods, thus
proposing a new way of discovering and validating such relations. The analysis
is performed for tabulated hadronic, hyperonic, and hybrid EoS-ensembles that
obey the multimessenger constraints and cover a wide range of stiffnesses. The
relations discussed could provide an accurate tool to constrain the equation of
state of nuclear matter when measurements of the relevant observables become
available.
