# Data-driven uncertainty-aware seakeeping prediction of the Delft 372 catamaran using ensemble Hankel dynamic mode decomposition

arXiv: https://arxiv.org/abs/2511.04461 · HTML: https://mlsanity.com/paper/2511.04461/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 64 references that are in the map.
- First posted on arXiv 6 Nov 2025.

## Builds on (most cited first, 9 of 9)

- [Modern Koopman Theory for Dynamical Systems](https://mlsanity.com/paper/2102.12086/) (2021)
- [Dynamic mode decomposition with control](https://mlsanity.com/paper/1409.6358/) (2014)
- [Extracting spatial-temporal coherent patterns in large-scale neural recordings using dynamic mode decomposition](https://mlsanity.com/paper/1409.5496/) (2014)
- [Bagging, optimized dynamic mode decomposition (BOP-DMD) for robust, stable forecasting with spatial and temporal uncertainty-quantification](https://mlsanity.com/paper/2107.10878/) (2021)
- [Data-Driven System Identification of 6-DoF Ship Motion in Waves with Neural Networks](https://mlsanity.com/paper/2111.01773/) (2021)
- [Predicting Ship Responses in Different Seaways using a Generalizable Force Correcting Machine Learning Method](https://mlsanity.com/paper/2405.08033/) (2024)
- [Analysis, forecasting and system identification of a floating offshore wind turbine using dynamic mode decomposition](https://mlsanity.com/paper/2411.07263/) (2024)
- [Model-free system identification of surface ships in waves via Hankel dynamic mode decomposition with control](https://mlsanity.com/paper/2502.15782/) (2025)
- [Bayesian dynamic mode decomposition for real-time ship motion digital twinning](https://mlsanity.com/paper/2411.14839/) (2024)

## Abstract (arXiv)

In this study, we present and validate an ensemble-based Hankel Dynamic Mode Decomposition with control (HDMDc) for uncertainty-aware seakeeping predictions of a high-speed catamaran, namely the Delft 372 model. Experimental measurements (time histories) of wave elevation at the longitudinal center of gravity, heave, pitch, notional flight-deck velocity, notional bridge acceleration, and total resistance were collected from irregular wave basin tests on a 1:33.3 scale replica of the Delft 372 model under sea state 5 conditions at Fr = 0.425, and organized into training, validation, and test sets. The HDMDc algorithm constructs an equation-free linear reduced-order model of the seakeeping vessel by augmenting states and inputs with their time-lagged copies to capture nonlinear and memory effects. Two ensembling strategies, namely Bayesian HDMDc (BHDMDc), which samples hyperparameters considered stochastic variables with prior distribution to produce posterior mean forecasts with confidence intervals, and Frequentist HDMDc (FHDMDc), which aggregates multiple model obtained over data subsets, are compared in providing seakeeping prediction and uncertainty quantification. The FHDMDc approach is found to improve the accuracy of the predictions compared to the deterministic counterpart, also providing robust uncertainty estimation; whereas the application of BHDMDc to the present test case is not found beneficial in comparison to the deterministic model. FHDMDc-derived probability density functions for the motions closely match both experimental data and URANS results, demonstrating reliable and computationally efficient seakeeping prediction for design and operational support.