  Long Short-Term Memory (LSTM) network-driven Non-Intrusive Reduced Order
Model (NROM) for predicting the dynamics of a floating box on the water surface
in a wavemaker basin is addressed in this study. The ground truth or actual
data for these wave-structure interactions (WSI) problems, namely box
displacements and hydrodynamic forces and moments acting on the box due to wave
interaction corresponding to a particular wave profile, are computed using the
Smoothed Particle Hydrodynamics (SPH). The dimensionality of the system is
first reduced using the Discrete Empirical Interpolation Method (DEIM) and the
LSTM is applied to the reduced system resulting in a DEIM-LSTM network for
developing a surrogate for prediction. The network is further enhanced by
incorporating the physics information into the loss function resulting in a
physics-informed LSTM (LSTM-PINN) for predicting the rigid body dynamics of box
motion. The performance of predictions for these networks is assessed for the
two-dimensional wave basin WSI problem as a proof-of-concept demonstration.
