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Item type:Publication, Access status: with files , Workshop numerical methods in geotechnics: AI in geotechnics, deformation based design, numerical optimization(Technische Universität Hamburg, 2026-09-17); ; ; ; The Workshop “Numerical Methods in Geotechnics”, held on 17–18 September 2026 in Hamburg, Germany, brings together researchers and practitioners to discuss recent developments in computational geotechnics. The workshop focuses on three main topics: artificial intelligence in geotechnics, deformation-based design, and numerical optimization. The contributions presented in these proceedings cover data-driven and AI-assisted modelling, surrogate models, advanced finite element methods, uncertainty quantification, soil–structure interaction, and optimization approaches in geotechnical engineering. Together, they highlight current advances in numerical methods and their growing importance for more predictive, efficient, and reliable geotechnical analysis and design.Publicationtype: Conference Proceedings125 66 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , From internal resonance to kinking and stacking in the response of nonlinear oscillatorsIn nonlinear dynamical systems, internal resonance is a well known phenomenon. Natural frequencies may change with vibration amplitude, becoming commensurable or close at certain energy levels. Nonlinear modes then interact based on their participating oscillators. Here, we investigate effects on the response curve of a system as neighboring oscillators are modified. This provides new insights into the relation of internal resonance and the frequency response of coupled nonlinear systems. We use methods of Harmonic Balance and a continuation algorithm to compute nonlinear frequency response curves and nonlinear normal modes for a model system. It is arranged as a chain of Duffing oscillators with separated natural frequencies. We demonstrate a kinking effect in the response curve once internal resonance emerges in the system’s forced response. The kink is observed as a shift to higher response levels, stretching the response curve. We also show how this kinking effect is modified by adapting the nonlinearities of neighboring oscillators. The system’s response branches kink vertically as an oscillator in the chain is made linear, stacking on top of each other. Multiple stable solutions at a certain frequency may then be observed. We show that these solutions correspond to varying degrees of vibration localization in the system. We expect our results to be valuable in system design. It should be possible to exploit the observed kinking effect, shaping the response curve by adding suitable oscillators of various characteristics. The engineer may thus be able to reach a target system response level at a certain frequency.Publicationtype: Journal Article39 46 - Some of the metrics are blocked by yourconsent settings
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 6 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Angle modulation phase shift in vibro-acoustic modulation: a novel approach for early crack detection(Multidisciplinary Digital Publishing Institute (MDPI), 2026-01-09); ; Detecting structural defects is one of the primary challenges engineers face. Consequently, the development of techniques and methods capable of detecting structural defects has always been critical. It should be emphasized that crack detection is only meaningful if it occurs before the final stages of structural failure. Accordingly, the early identification of structural defects has become a significant research challenge, motivating the development of techniques and diagnostic parameters that can effectively capture and reflect the structure’s nonlinearity or non-uniform behavior. This study aims to provide a more detailed examination of modulation phenomena observed in the measured response using the vibro-acoustic modulation (VAM) method, and propose a new model that simultaneously incorporates all three conventional modulation types (amplitude, frequency, and phase), which may offer a more accurate representation of the response signal behavior. Both theoretical and experimental results clearly confirm that the phase shifts of individual frequency components in the frequency domain vary throughout the lifetime of the tested specimen. This behavior, as anticipated by the proposed model, reveals a strong correlation between phase shifts and modulation indices (MIs). Furthermore, the relative sensitivity analysis indicates that the phase shift is more sensitive than the modulation index (MI), suggesting its strong potential as an indicator for early defect detection in structural components.Publicationtype: Journal Article17 13 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , A network‐based approach to identifying key components in structural vibrationsModeling the dynamics of systems with many interacting components, such as robots, wind turbines, and trusses, remains challenging today. These systems often display complex oscillatory responses to external inputs, and harmful vibrations might be excited along with the desired motion. Understanding the relative importance of individual components or systems aspects to the overall system dynamics could be a vital step towards focused design and maintenance efforts. This work proposes a network‐based approach to studying the dynamics of a mechanical system by representing the system as a network of coupled oscillators, where each node corresponds to a machine component and each link denotes a physical connection, such as a weld or bolt. Inspired by studies of dynamics in biological and social networks, we show how network measures can be used to predict the importance of a single oscillator, or component, for shaping the overall dynamics. We further demonstrate under which conditions these conclusions are possible, and where the metrics fail. This study hopes to contribute to the broader field of network‐based methods in engineering and yield insights that help focus design and maintenance efforts in the future.Publicationtype: Journal Article21 7 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Bridging ocean wave physics and deep learning: physics-informed neural operators for nonlinear wavefield reconstruction in real-time(American Institute of Physics (AIP), 2025-10-07); ; Accurate real-time reconstruction of phase-resolved ocean wave fields remains a critical yet largely unsolved problem, primarily due to the absence of practical data assimilation methods for obtaining initial conditions from sparse or indirect wave measurements. While recent advances in supervised deep learning have shown potential for this purpose, they require large labeled datasets of ground truth wave data, which are infeasible to obtain in real-world scenarios. To overcome this limitation, we propose a physics-informed neural operator (PINO) framework for reconstructing spatially and temporally phase-resolved, nonlinear ocean wave fields from sparse measurements, without the need for ground truth data during training. This is achieved by embedding residuals of the free surface boundary conditions of ocean gravity waves into the loss function, constraining the solution space in a soft manner. In the current implementation, the framework is demonstrated for long-crested, unidirectional wave surfaces, where the wave propagation direction is aligned with the radar scanning direction. Within this setting, we validate our approach using highly realistic synthetic wave measurements by demonstrating the accurate reconstruction of nonlinear wave fields from both buoy time series and radar snapshots. Our results indicate that PINOs enable accurate, real-time reconstruction and generalize robustly across a wide range of wave conditions, thereby paving the way for future extensions of this framework toward multidirectional sea states and thus operational wave reconstruction in realistic marine environments.Publicationtype: Journal Article28 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Physics-informed neural networks for phase-resolved data assimilation and prediction of nonlinear ocean waves(American Physical Society (APS), 2025-09-02); ; ; ; Cao, RuiThe assimilation and prediction of phase-resolved surface gravity waves are critical challenges in ocean science and engineering. Potential flow theory (PFT) has been widely employed to develop wave models and numerical techniques for wave prediction. However, traditional wave prediction methods are often limited. For example, most simplified wave models have a limited ability to capture strong wave nonlinearity, while fully nonlinear PFT solvers often fail to meet the speed requirements of engineering applications. This computational inefficiency also hinders the development of effective data assimilation techniques, which are required to reconstruct spatial wave information from sparse measurements to initialize the wave prediction. To address these challenges, we propose a solver method that leverages physics-informed neural networks (PINNs) that parametrize PFT solutions as neural networks. This provides a computationally inexpensive way to assimilate and predict wave data. The proposed PINN framework is validated through comparisons with analytical linear PFT solutions and experimental data collected in a laboratory wave flume. The results demonstrate that our approach accurately captures and predicts irregular, nonlinear, and dispersive wave surface dynamics. Moreover, the PINN can infer the fully nonlinear velocity potential throughout the entire fluid volume solely from surface elevation measurements, enabling the calculation of fluid velocities that are difficult to measure experimentally.Publicationtype: Journal Article15 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Exploitation of modal superposition toward forced vibration localization in a coupled symmetric oscillator array(Multidisciplinary Digital Publishing Institute (MDPI), 2025-05-14); ; ; In transducer arrays, symmetric grouping of identical elements is often employed to achieve uniform array performance. Such arrays can possess high coupling, preventing localized operation of individual transducers. This paper provides insight into how forced vibration localizes in a symmetric system of coupled oscillators. We use a simple lumped-parameter model of highly coupled oscillators derived from ultrasound transducer arrays. Forced vibration localization can be shown to be inversely related to the coupling strength between the oscillators. The results demonstrate how forced vibration in a coupled symmetric system may localize through modal superposition and how it may be tuned via the spacing of natural frequencies. Breaking the system’s symmetry leads to normal mode localization, which can be shown to affect the forced vibration response. The results reveal a variation in the system’s resonance frequency, attributed to curve veering effects.Publicationtype: Journal Article25 17 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Exploring localization in nonlinear oscillator systems through network-based predictionsLocalized vibrations, arising from nonlinearities or symmetry breaking, pose a challenge in engineering, as the resulting high-amplitude vibrations may result in component failure due to fatigue. During operation, the emergence of localization is difficult to predict, partly because of changing parameters over the life cycle of a system. This work proposes a novel, network-based approach to detect an imminent localized vibration. Synthetic measurement data are used to generate a functional network, which captures the dynamic interplay of the machine parts, complementary to their geometric coupling. Analysis of these functional networks reveals an impending localized vibration and its location. The method is demonstrated using a model system for a bladed disk, a ring composed of coupled nonlinear Duffing oscillators. Results indicate that the proposed method is robust against small parameter uncertainties, added measurement noise, and the length of the measurement data samples. The source code for this work is available at C. Geier [(2024). “Code for paper Exploring localization in nonlinear oscillator systems through network-based predictions,” Zenodo. https://doi.org/10.5281/zenodo.12611988].Publicationtype: Journal Article19 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , The influence of the modeling order on the predictable area of non-linear unidirectional ocean waves(Elsevier, 2025-04-30); ; ; ; The influence of non-linear modeling of phase-resolved ocean wave fields on the extent of the accessible predictable area is investigated. Assuming that the ocean surface dynamics is known over a limited spatial domain, e.g. via radar backscatter reconstruction, the linear wave theory as well as the high-order spectral method with various orders of non-linearity are used to propagate the surface with different levels of physical fidelity. The prediction accuracy is quantified by comparing the predicted waves to a reference, i.e. a fully known wave field propagated with a high-fidelity wave model. By doing this, it is made possible to track the spatiotemporal evolution of the prediction accuracy and define the predictable area as the region over which the accuracy is higher than a threshold, here defined by a “surface similarity parameter” lower than 0.1. Different unidirectional wave field characteristics are studied, highlighting the effect of the wave steepness, water depth and wave energy spreading around the peak spectral frequency, all impacting significantly the prediction accuracy, thus the predictable area. It is shown that the extent of the predictable area is highly dependent on the order of the considered wave model, and that the third order generally leads to the largest reachable predictable area in all configurations.Publicationtype: Journal Article15 11