16 citations · 30 across the 5 of their papers we have counts for
5 papers
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…
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…
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…