Abstract Machine learning (ML) and deep learning (DL) have significantly advanced maritime engineering and hydrodynamics by enabling data-driven modelling and prediction. In hydrodynamics, high-fidelity surrogates offer precision, but low-fidelity surrogates are often preferred for generating large training sets due to their low computational cost. Time series records are a central form of data across seakeeping, structural analysis, sea-state estimation, ship-response prediction, control, and manoeuvring; they encode both the system’s response to excitation and key vessel characteristics. To improve robustness, coverage, and perturbation resilience, augmentation techniques, such as jittering, scaling, warping, and permutation, are commonly applied, but typically tuned by trial-and-error and rarely grounded in physics. To address this, we introduce a novel feature-engineering methodology that derives a physics-informed jitter variance from experiment–simulation discrepancies in time–frequency features, turning jittering from a heuristic into a principled step. The method is evaluated on a spherical floating model tested in regular and irregular unidirectional waves and compared against numerical simulations. Beyond PDF/PSD-based representational fidelity comparison, the augmented datasets are further assessed through downstream simulation-to-experiment classification across benchmark ML/DL models. Compared with raw simulation and RMS-matched standard Gaussian jittering, the proposed physics-informed augmentation improved experimental test accuracy and macro-F1 across most model families. These results demonstrate that the proposed method is not merely a spectral noise adder, but a physics-guided augmentation strategy that improves the ML usability of low-fidelity hydrodynamic time series.
Majidiyan et al. (Sat,) studied this question.