Deep learning-driven nonlinear reduced-order models for predicting wave-structure interaction

Abstract

The feasibility of using a Long Short-Term Memory (LSTM) network-driven Non-Intrusive Reduced Order Model (NIROM) to predict the dynamics of a two-dimensional box floating and interacting with surface water waves is assessed in this study. The ground for these wave-structure interactions (WSI) problems, namely box displacements and hydrodynamic forces arising from wave interaction corresponding to a particular surface wave profile, are computed using a single-phase 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. This 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 these networks in predicting the dynamics of the floating box is compared as a proof-of-concept demonstration.