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Long Short-Term Memory (LSTM) network-driven Non-Intrusive Reduced OrderModel (NROM) for predicting the dynamics of a floating box on the water surfacein a wavemaker basin is addressed in this study. The ground truth or actualdata for these wave-structure interactions (WSI) problems, namely boxdisplacements and hydrodynamic forces and moments acting on the box due to waveinteraction corresponding to a particular wave profile, are computed using theSmoothed Particle Hydrodynamics (SPH). The dimensionality of the system isfirst reduced using the Discrete Empirical Interpolation Method (DEIM) and theLSTM is applied to the reduced system resulting in a DEIM-LSTM network fordeveloping a surrogate for prediction. The network is further enhanced byincorporating the physics information into the loss function resulting in aphysics-informed LSTM (LSTM-PINN) for predicting the rigid body dynamics of boxmotion. The performance of predictions for these networks is assessed for thetwo-dimensional wave basin WSI problem as a proof-of-concept demonstration.

Deep Learning-Driven Nonlinear Reduced-Order Models for Predicting Wave-Structure Interaction