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Item type:Publication, Access status: with files , Experimental evaluation of data-driven deterministic wave generation(American Society of Mechanical Engineers (ASME), 2026-06); ; ; ; This paper explores the applicability of data-driven deterministic wave generation in an experimental environment. Various datasets that comprise different combinations of experimental- and synthetic data are used to train a fully convolutional neural network on the task of predicting the wave-maker control signal associated to a desired target wave sequence at a predefined target location in the wave tank. The trained models are evaluated in an experimental program, where the predicted control signals are executed in a wave tank, the actual wave is measured and compared against the predefined target wave. The standard wave modeling approach for the generation of tailored wave sequences, which is based on linear theory, is conducted for reference. The test dataset contains 950 irregular sea states with a wide range of characteristic sea state parameters, as well as seven tailored extreme waves, including the well-known “Draupner Wave”. It is shown that the data-driven approach consistently outperforms the standard method in accuracy across the studied range of sea states, emphasizing its effectiveness as a flexible and precise alternative for wave basin experiments.Publicationtype: Conference Paper29 5 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Data-driven, non-linear ship response prediction based on time series of irregular, long-crested sea states amidships(Elsevier, 2025-02-01); ; ; ; The accurate prediction of vessel responses in waves is crucial for decision-making and contribute to the operational safety and risk minimization. Short-term predictions can be carried out by estimating the vessel's motions and loads based on incident waves. Existing model-based approaches either require computationally intensive simulations that compromise real-time capability or use simplified models affecting the accuracy of the prediction. Therefore, this study explores the feasibility of using neural networks for mapping time signals of surface elevation data and a set of corresponding ship responses, i.e. the heave and pitch motions as well as the vertical bending moment. The approach followed here is built on the assumption that the wave profile amidships is known. A synthetic dataset was generated using a time-domain strip theory solver with considerations of non-linear effects on motions and loads due to large amplitude waves in a variety of irregular, long-crested sea state conditions. We propose two different neural network models, a multi-layer perceptron (MLP) and a fully convolutional neural network (FCNN), and compare their performances on measurement data obtained from model tests in a seakeeping basin. The evaluations also include the freak wave reproduction of the ‘new year wave’. The proposed networks are able to estimate the motions and bending moment accurately for a wide range of sea state conditions, surpassing current state-of-the-art models on the given data sets.Publicationtype: Journal Article78 97 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Machine learning for phase-resolved reconstruction of nonlinear ocean wave surface elevations from sparse remote sensing data(Elsevier, 2023-11-15); ; ; ; Accurate short-term predictions of phase-resolved water wave conditions are crucial for decision-making in ocean engineering. However, the initialization of remote-sensing-based wave prediction models first requires a reconstruction of wave surfaces from sparse measurements like radar. Existing reconstruction methods either rely on computationally intensive optimization procedures or simplistic modelling assumptions that compromise the real-time capability or accuracy of the subsequent prediction process. We therefore address these issues by proposing a novel approach for phase-resolved wave surface reconstruction using neural networks based on the U-Net and Fourier neural operator (FNO) architectures. Our approach utilizes synthetic yet highly realistic training data on uniform one-dimensional grids, that is generated by the high-order spectral method for wave simulation and a geometric radar modelling approach. The investigation reveals that both models deliver accurate wave reconstruction results and show good generalization for different sea states when trained with spatio-temporal radar data containing multiple historic radar snapshots in each input. Notably, the FNO demonstrates superior performance in handling the data structure imposed by wave physics due to its global approach to learn the mapping between input and output in Fourier space.Publicationtype: Journal Article28 95 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Surface similarity parameter : a new machine learning loss metric for oscillatory spatio-temporal dataSupervised machine learning approaches require the formulation of a loss functional to be minimized in the training phase. Sequential data are ubiquitous across many fields of research, and are often treated with Euclidean distance-based loss functions that were designed for tabular data. For smooth oscillatory data, those conventional approaches lack the ability to penalize amplitude, frequency and phase prediction errors at the same time, and tend to be biased towards amplitude errors. We introduce the surface similarity parameter (SSP) as a novel loss function that is especially useful for training machine learning models on smooth oscillatory sequences. Our extensive experiments on chaotic spatio-temporal dynamical systems indicate that the SSP is beneficial for shaping gradients, thereby accelerating the training process, reducing the final prediction error, increasing weight initialization robustness, and implementing a stronger regularization effect compared to using classical loss functions. The results indicate the potential of the novel loss metric particularly for highly complex and chaotic data, such as data stemming from the nonlinear two-dimensional Kuramoto–Sivashinsky equation and the linear propagation of dispersive surface gravity waves in fluids.Publicationtype: Journal ArticleScopus© Citations 5 52 57