37 citations · 107 across the 18 of their papers we have counts for
19 papers
AFT Neural Function Approximators for 1D Nonlinear Force Laws
Miriam Goldack, Johann Groß, Malte Krack +1
Nonlinear contacts and friction strongly influence the vibration response of assembled structures, but their accurate numerical treatment is computationally demanding. The harmonic…
Deep learning-based prediction of time-resolved adhesive forces in viscoelastic Hertzian contacts
Ali Maghami, Merten Stender, Michele Ciavarella +1
Fast prediction of the response of adhesive soft viscoelastic contacts represents a current challenge in soft robotics and for gripping and manipulation tasks. Determining the comp…
Dynamics-Informed Reservoir Computing with Visibility Graphs
Charlotte Geier, Rasha Shanaz, Merten Stender
Accurate prediction of complex and nonlinear time series remains a challenging problem across engineering and scientific disciplines. Reservoir computing (RC) offers a computationa…
Bridging ocean wave physics and deep learning: Physics-informed neural operators for nonlinear wavefield reconstruction in real-time
Svenja Ehlers, Merten Stender, Norbert Hoffmann
Accurate real-time prediction of phase-resolved ocean wave fields remains a critical yet largely unsolved problem, primarily due to the absence of practical data assimilation metho…
Pull-off force prediction in viscoelastic adhesive Hertzian contact by physics augmented machine learning
Ali Maghami, Merten Stender, Antonio Papangelo
Understanding and predicting the adhesive properties of viscoelastic Hertzian contacts is crucial for diverse engineering applications, including robotics, biomechanics, and advanc…
Node pruning reveals compact and optimal substructures within large networks
Manish Yadav, Merten Stender
The structural complexity of reservoir networks poses a significant challenge, often leading to excessive computational costs and suboptimal performance. In this study, we introduc…
Denoising and Reconstruction of Nonlinear Dynamics using Truncated Reservoir Computing
Omid Sedehi, Manish Yadav, Merten Stender +1
Measurements acquired from distributed physical systems are often sparse and noisy. Therefore, signal processing and system identification tools are required to mitigate noise effe…
Physics-informed neural networks for phase-resolved data assimilation and prediction of nonlinear ocean waves
Svenja Ehlers, Norbert Hoffmann, Tianning Tang +5
The assimilation and prediction of phase-resolved surface gravity waves are critical challenges in ocean science and engineering. Potential flow theory (PFT) has been widely employ…
The impact of AI on engineering design procedures for dynamical systems
Kristin M. de Payrebrune, Kathrin Flaßkamp, Tom Ströhla +19
Artificial intelligence (AI) is driving transformative changes across numerous fields, revolutionizing conventional processes and creating new opportunities for innovation. The dev…
Data Publishing in Mechanics and Dynamics: Challenges, Guidelines, and Examples from Engineering Design
Henrik Ebel, Jan van Delden, Timo Lüddecke +15
Data-based methods have gained increasing importance in engineering, especially but not only driven by successes with deep artificial neural networks. Success stories are prevalent…
Predicting multi-parametric dynamics of externally forced oscillator using reservoir computing and minimal data
Manish Yadav, Swati Chauhan, Manish Dev Shrimali +1
Mechanical systems exhibit complex dynamical behavior from harmonic oscillations to chaotic motion. The dynamics undergo qualitative changes due to changes to internal system param…
Evolution beats random chance: Performance-dependent network evolution for enhanced computational capacity
Manish Yadav, Sudeshna Sinha, Merten Stender
The quest to understand structure-function relationships in networks across scientific disciplines has intensified. However, the optimal network architecture remains elusive, parti…
Data assimilation and parameter identification for water waves using the nonlinear Schrödinger equation and physics-informed neural networks
Svenja Ehlers, Niklas A. Wagner, Annamaria Scherzl +3
The measurement of deep water gravity wave elevations using in-situ devices, such as wave gauges, typically yields spatially sparse data. This sparsity arises from the deployment o…
Machine learning for phase-resolved reconstruction of nonlinear ocean wave surface elevations from sparse remote sensing data
Svenja Ehlers, Marco Klein, Alexander Heinlein +4
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 wa…
Surface Similarity Parameter: A New Machine Learning Loss Metric for Oscillatory Spatio-Temporal Data
Mathies Wedler, Merten Stender, Marco Klein +2
Supervised 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 rese…
Deep learning for brake squeal: vibration detection, characterization and prediction
Merten Stender, Merten Tiedemann, David Spieler +3
Despite significant advances in modeling of friction-induced vibrations and brake squeal, the majority of industrial research and design is still conducted experimentally, since ma…
Energy harvesting below the onset of flutter
Merten Stender, Merten Tiedemann, Norbert Hoffmann
This work demonstrates preliminary results on energy harvesting from a linearly stable flutter-type system with circulatory friction forces. Harmonic external forcing is applied to…
The non-linear behavior of aqueous model ice in downward flexure
Rüdiger U. Franz von Bock und Polach, Robert Ettema, Silke Gralher +2
As aqueous model ice is used extensively in ice tanks tests on the performance of ship hulls in sheet ice, it is imperative that such model ice replicate the main flexural strength…
Establishing a common data base of ice experiments and using machine learning to understand and predict ice behavior
Leon Kellner, Merten Stender, Hauke Herrnring +4
Machine learning and statistical tools are applied to identify how parameters, such as temperature, influence peak stress and ice behavior. To enable the analysis, a common and sma…