16 citations · 37 across the 14 of their papers we have counts for
6 papers · 1 filter
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…