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Machine learning for phase-resolved reconstruction of nonlinear ocean wave surface elevations from sparse remote sensing data

Ehlers, Svenja and Klein, Marco and Henlein, Alexander and Wedler, Mathies and Desmars, Nicolas and Hofmann, Norbert and Stender, Merten (2023) Machine learning for phase-resolved reconstruction of nonlinear ocean wave surface elevations from sparse remote sensing data. Ocean Engineering (288). Elsevier. doi: 10.1016/j.oceaneng.2023.116059. ISSN 0029-8018.

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Official URL: https://www.sciencedirect.com/science/article/pii/S0029801823024435


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 wave prediction models first requires a reconstruction of wave surfaces from sparse measurements like radar. Existing reconstruction methods either rely on computationally intensive optimization procedures or simplistic modelling assumptions that compromise the real-time capability or accuracy of the subsequent prediction process. We therefore address these issues by proposing a novel approach for phase-resolved wave surface reconstruction using neural networks based on the U-Net and Fourier neural operator (FNO) architectures. Our approach utilizes synthetic yet highly realistic training data on uniform one-dimensional grids, that is generated by the high-order spectral method for wave simulation and a geometric radar modelling approach. The investigation reveals that both models deliver accurate wave reconstruction results and show good generalization for different sea states when trained with spatio-temporal radar data containing multiple historic radar snapshots in each input. Notably, the FNO demonstrates superior performance in handling the data structure imposed by wave physics due to its global approach to learn the mapping between input and output in Fourier space.

Item URL in elib:https://elib.dlr.de/198238/
Document Type:Article
Title:Machine learning for phase-resolved reconstruction of nonlinear ocean wave surface elevations from sparse remote sensing data
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Ehlers, SvenjaTechnische Universität HamburgUNSPECIFIEDUNSPECIFIED
Klein, MarcoUNSPECIFIEDhttps://orcid.org/0000-0003-2867-7534144691211
Henlein, AlexanderDelft University of TechnologyUNSPECIFIEDUNSPECIFIED
Hofmann, NorbertTechnische Universität HamburgUNSPECIFIEDUNSPECIFIED
Stender, MertenTechnische Universität BerlinUNSPECIFIEDUNSPECIFIED
Date:15 November 2023
Journal or Publication Title:Ocean Engineering
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In ISI Web of Science:Yes
Keywords:Deep operator learning Fourier neural operator Nonlinear ocean waves Phase-resolved surface reconstruction X-band radar images Radar inversion
HGF - Research field:Energy
HGF - Program:Energy System Design
HGF - Program Themes:Digitalization and System Technology
DLR - Research area:Energy
DLR - Program:E SY - Energy System Technology and Analysis
DLR - Research theme (Project):E - Energy System Technology
Location: Geesthacht
Institutes and Institutions:Institute of Maritime Energy Systems
Deposited By: Klein, Marco
Deposited On:18 Oct 2023 07:31
Last Modified:18 Oct 2023 07:31

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