  Non-intrusive model reduction is a promising solution to system dynamics
prediction, especially in cases where data are collected from experimental
campaigns or proprietary software simulations. In this work, we present a
method for non-intrusive model reduction applied to Fluid-Structure Interaction
(FSI) problems. The approach is based on the a priori known sparsity of the
full-order system operators, which is dictated by grid adjacency information.
In order to enforce this type of sparsity, we solve a local, regularized
least-squares problem for each degree of freedom on a grid, considering only
the training data from adjacent degrees of freedom, thus making computation and
storage of the inferred full-order operators feasible. After constructing the
non-intrusive, sparse full-order model, Proper Orthogonal Decomposition (POD)
is used for its projection to a reduced dimension subspace and thus the
construction of a reduced-order model (ROM). The methodology is applied to the
challenging Hron-Turek benchmark FSI3, for Re = 200. A physics-informed,
non-intrusive ROM is constructed to predict the two-way coupled dynamics of a
solid with a deformable, slender tail, subject to an incompressible, laminar
flow. Results considering the accuracy and predictive capabilities of the
inferred reduced models are discussed.
