3 papers
cs.LG2025
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…
cs.LG2025
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…
physics.flu-dyn2024
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…