  We present a hybrid physics-based deep learning (DL) framework for handling
moving interfaces and predicting fluid-structure interaction (FSI). Using the
discretized Navier-Stokes in the Arbitrary Lagrangian-Eulerian (ALE) reference
frame, we generate full-order flow snapshots and point-cloud displacements as
target physical data for the learning and inference of fluid-structure
dynamics. This integrated operation of the physics-based modeling with the
DL-based reduced-order model (DL-ROM) makes our framework hybrid. This
multi-level framework is composed of two data-driven physics-DL drivers that
predict unsteady flow and track the moving point cloud displacements
respectively, while synchronously exchange the force information at the
interface. The first component relies on the proper orthogonal
decomposition-based recurrent neural network (POD-RNN) as a semi-supervised
procedure to infer the point cloud ALE description. This model essentially
relies on the POD basis modes to reduce dimensionality and evolving them in the
time domain of RNN. The second component utilizes the convolution-based
recurrent autoencoder network (CRAN) as a self-supervised DL procedure to
predict the nonlinear flow dynamics at static Eulerian probes. We introduce
these probes as spatially structured query nodes in the moving point cloud to
resolve the field Lagrangian to Eulerian conflict and conveniently train the
CRAN driver. We design a novel snapshot-field transfer and load recovery (FTLR)
algorithm to optimally select the Eulerian probes by recovering bulk force
quantities. A prototypical problem of flow past a freely oscillating cylinder
is selected to test the efficacy of the proposed methodology. The framework
tracks the interface description and predicts highly non-linear wake dynamics
for nearly 500 time-steps. These results further the application of digital
twinning of FSI engineering systems.
