  Stable partitioned techniques for simulating unsteady fluid-structure
interaction (FSI) are known to be computationally expensive when high
added-mass is involved. Multiple coupling strategies have been developed to
accelerate these simulations, but often use predictors in the form of simple
finite-difference extrapolations. In this work, we propose a non-intrusive
data-driven predictor that couples reduced-order models of both the solid and
fluid subproblems, providing an initial guess for the nonlinear problem of the
next time step calculation. Each reduced order model is composed of a nonlinear
encoder-regressor-decoder architecture and is equipped with an adaptive update
strategy that adds robustness for extrapolation. In doing so, the proposed
methodology leverages physics-based insights from high-fidelity solvers, thus
establishing a physics-aware machine learning predictor. Using three strongly
coupled FSI examples, this study demonstrates the improved convergence obtained
with the new predictor and the overall computational speedup realized compared
to classical approaches.
