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

The feasibility of using a Long Short-Term Memory (LSTM) network-driven Non-Intrusive Reduced Order Model (NIROM) to predict the dynamics two-dimensional box floating and interacting with surface water waves is assessed in this study. ground for these wave-structure interactions (WSI) problems, namely displacements hydrodynamic forces arising from wave interaction corresponding particular profile, are computed single-phase Smoothed Particle Hydrodynamics (SPH). dimensionality system first reduced Discrete Empirical Interpolation Method (DEIM) LSTM applied resulting DEIM-LSTM network developing surrogate prediction. This further enhanced by incorporating physics information into loss function physics-informed (LSTM-PINN) predicting rigid body motion. performance networks compared as proof-of-concept demonstration.