  The main goal of this work is to develop a data-driven Reduced Order Model
(ROM) strategy from high-fidelity simulation result data of a Full Order Model
(FOM). The goal is to predict at lower computational cost the time evolution of
solutions of Fluid-Structure Interaction (FSI) problems.
  For some FSI applications, the elastic solid FOM (often chosen as
quasi-static) can take far more computational time than the fluid one. In this
context, for the sake of performance one could only derive a ROM for the
structure and try to achieve a partitioned FOM fluid solver coupled with a ROM
solid one. In this paper, we present a data-driven partitioned ROM on two study
cases: (i) a simplified 1D-1D FSI problem representing an axisymmetric elastic
model of an arterial vessel, coupled with an incompressible fluid flow; (ii) an
incompressible 2D wake flow over a cylinder facing an elastic solid with two
flaps. We evaluate the accuracy and performance of the proposed ROM-FOM
strategy on these cases while investigating the effects of the model's
hyperparameters. We demonstrate a high prediction accuracy and significant
speedup achievements using this strategy.
