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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: without files , Data-driven generation of tailored wave sequences(American Society of Mechanical Engineers (ASME), 2024-06); ; ; ; This paper explores the applicability of machine learning techniques for the generation of tailored wave sequences. For this purpose, a fully convolutional neural network was implemented for relating the target wave sequence at the target location in time domain to the respective control signal of the wave board. The database was generated by means of extensive wave tank tests. The experimental campaign focused on the generation of very steep wave groups including wave breaking which cannot be covered by the simplified wave generation methods. The extensive experimental campaign was performed in a small wave tank with an fully automated approach including determination and control of the wave maker motion as well as data measurement. The training data set features wave groups of short duration based on JONSWAP spectra, where the parameters wave steepness, peak wave period and enhancement factor were systematically varied. At the end of the training process, the trained models are able to predict the wave maker control signal based on time series of the target wave defined for a specific target location in the wave tank. The accuracy of the trained models are evaluated by means of unseen validation data. In addition, the predictive accuracy of the trained models is compared with the classical linear transformation approach.Publicationtype: Conference Paper56 - 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: without files , Machine learning simulation of one-dimensional deterministic water wave propagation(2023-09-15); ; ; Deterministic phase-resolved prediction of the evolution of surface gravity waves in water is challenging due to their complex spatio-temporal dynamics. Physics-based methods of varying complexity are available, but the conflicting objectives of numerical efficiency and accuracy impede real-time wave prediction. Data-driven methods may be able to overcome this challenge by using training data generated by complex numerical methods. This work explores the potential of a machine learning (ML) approach based on a fully convolutional encoder–decoder architecture for the efficient and accurate prediction of water waves. The high-order spectral (HOS) method forms the foundation for the generation of the training data. The HOS method is applied for different, consecutive orders of nonlinearity starting from first order up to fourth order. The JONSWAP wave energy spectrum serves as the basis for modeling the one-dimensional irregular sea states. The overall objective of this work is to evaluate whether the complex non-linear physical processes can be identified and learned by the ML approach. The trained ML flow mapper is used to perform time integration of an initial sea state. The results indicate that the proposed ML approach is able to reproduce the distinctive physical processes of the different orders of nonlinearities. It is shown that the ML approach enables fast and accurate predictions of one-dimensional waves over a time horizon that spans multiple peak periods.Publicationtype: Journal Article22 - 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 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Application of machine learning for the generation of tailored wave sequences(American Society of Mechanical Engineers (ASME), 2022-06); ; ; ; This paper explores the applicability of machine learning techniques for the generation of tailored wave sequences. For this purpose, a fully convolutional neural network was implemented for relating the target wave sequence at the target location in time domain to the respective wave sequence at the wave board. The synthetic training and validation data were acquired by applying the high-order spectral (HOS) method. The HOS method is a very accurate method for modeling non-linear wave propagation and its numerical efficiency allows the generation of large synthetic data sets. The training data featured wave groups of short duration based on JONSWAP spectra. The sea state parameters wave steepness, wave period and enhancement factor were systematically varied. At the end of the training process, the trained models were able to predict the wave sequences at the wave board based on the time series of the target wave defined for a specific target location in the wave tank. The accuracy of the trained models were evaluated by means of unseen validation data. In addition, the predictive accuracy of the trained models was compared with the classical linear transformation approach.Publicationtype: Conference Paper72 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Explainable machine learning: A case study on impedance tube measurements(2021-08); ; ; Machine learning techniques allow for finding hidden patterns and signatures in data. Currently, these methods are gaining increased interest in engineering in general and in vibroacoustics in particular. Although ML methods are successfully applied, it is hardly understood how these black-box-type methods make their decisions. Explainable machine learning aims at overcoming this issue by deepen the understanding on the decision making process through perturbation-based model diagnosis. This paper introduces machine learning methods and reviews recent techniques for explainability and interpretability. These methods are exemplified on sound absorption coefficient spectra of one sound absorbing foam material measured in an impedance tube. Variances of the absorption coefficients measurements as a function of the specimen thickness and the operator are modeled by univariate and multivariate machine learning models. In order to identify the driving patterns, i.e., how and in which frequency regime the measurements are affected by the setup specifications, Shapley additive explanations are derived for the ML models. It is demonstrated how explaining machine learning models can be used to discover and express complicated relations in experimental data, thereby paving the way to novel knowledge discovery strategies in evidence-based modeling.Publicationtype: Conference Paper52 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Explainable machine learning determines effects on the sound absorption coefficient measured in the impedance tubeMeasurements of acoustic properties of sound absorbing materials in impedance tubes show poor reproducibility, which was demonstrated in round robin tests. The impedance tube measurements are standardized but lack precise definitions of the actual measurement setup, specimen preparation, and other factors that introduce uncertainty in practice. In this paper, machine learning models identify those factors that mostly affect the sound absorption coefficient from a large data set of more than 3000 absorption spectra measured in one impedance tube. The specimens are manufactured from one polyurethane foam, and different cutting technologies, different operators, different specimen diameters, different specimen thicknesses, and two different approaches to mount the specimens in the impedance tube are considered. Explainable machine learning techniques allow the identification and quantification of the most influential factors and, furthermore, the frequency ranges that are the most affected by the choice of these setup factors. The results indicate that besides the specimen thickness, also the operator affects the absorption coefficient by a directional and non-random relationship. Hence, it needs to be controlled carefully. The method proves to be a promising pathway for knowledge discovery from acoustic measurement data using explainability approaches for machine learning models.Publicationtype: Journal ArticleScopus© Citations 11 105