Data assimilation and parameter identification for water waves using the nonlinear Schr\"{o}dinger equation and physics-informed neural networks

• URL: http://arxiv.org/abs/2401.03708v1
• Date: Mon, 8 Jan 2024 07:35:48 GMT
• Title: Data assimilation and parameter identification for water waves using the nonlinear Schr\"{o}dinger equation and physics-informed neural networks
• Authors: Svenja Ehlers, Niklas A. Wagner, Annamaria Scherzl, Marco Klein, Norbert Hoffmann, Merten Stender
• Abstract summary: The measurement of deep water gravity wave elevations using in-situ devices, such as wave gauges, typically yields sparse data. This sparsity arises from the deployment of a limited number of gauges due to their installation effort and high operational costs. We propose the application of physics-informed neural network (PINN) to reconstruct physically consistent wave fields.
• Score: 0.3495246564946556
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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 of a limited number of gauges due to their installation effort and high operational costs. The reconstruction of the spatio-temporal extent of surface elevation poses an ill-posed data assimilation problem, challenging to solve with conventional numerical techniques. To address this issue, we propose the application of a physics-informed neural network (PINN), aiming to reconstruct physically consistent wave fields between two designated measurement locations several meters apart. Our method ensures this physical consistency by integrating residuals of the hydrodynamic nonlinear Schr\"{o}dinger equation (NLSE) into the PINN's loss function. Using synthetic wave elevation time series from distinct locations within a wave tank, we initially achieve successful reconstruction quality by employing constant, predetermined NLSE coefficients. However, the reconstruction quality is further improved by introducing NLSE coefficients as additional identifiable variables during PINN training. The results not only showcase a technically relevant application of the PINN method but also represent a pioneering step towards improving the initialization of deterministic wave prediction methods.

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