  Reduced order models (ROM) are commonly employed to solve parametric problems
and to devise inexpensive response surfaces to evaluate quantities of interest
in real-time. There are many families of ROMs in the literature and choosing
among them is not always a trivial task. This work presents a comparison of the
performance of a priori and a posteriori proper generalised decomposition (PGD)
algorithms for an incompressible Stokes flow problem in a geometrically
parametrised domain. This problem is particularly challenging as the geometric
parameters affect both the solution manifold and the computational spatial
domain. The difficulty is further increased because multiple geometric
parameters are considered and extended ranges of values are analysed for the
parameters and this leads to significant variations in the flow features. Using
a set of numerical experiments involving geometrically parametrised
microswimmers, the two PGD algorithms are extensively compared in terms of
their accuracy and their computational cost, expressed as a function of the
number of full-order solves required.
