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

• URL: http://arxiv.org/abs/2305.11913v2
• Date: Wed, 18 Oct 2023 07:02:02 GMT
• Title: Machine learning for phase-resolved reconstruction of nonlinear ocean wave surface elevations from sparse remote sensing data
• Authors: Svenja Ehlers, Marco Klein, Alexander Heinlein, Mathies Wedler, Nicolas Desmars, Norbert Hoffmann, Merten Stender
• Abstract summary: We propose a novel approach for phase-resolved wave surface reconstruction using neural networks. Our approach utilizes synthetic yet highly realistic training data on uniform one-dimensional grids.
• Score: 37.69303106863453
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Continual Learning of Range-Dependent Transmission Loss for Underwater Acoustic using Conditional Convolutional Neural Net [0.0]
  This research aims to improve the accuracy of deep-learning models for predicting underwater radiated noise in far-field scenarios. We propose a novel range-conditional convolutional neural network that incorporates ocean bathymetry data into the input. Our proposed architecture effectively captures transmission loss over a range-dependent, varying bathymetry profile.
  arXiv  Detail & Related papers  (2024-04-11T19:13:38Z)
• A Physics-informed machine learning model for time-dependent wave runup prediction [0.0]
  A physics-informed machine learning-based approach is proposed to efficiently and accurately simulate time-series wave runup. A conditional generative adversarial network (cGAN) is used to map the image representation of wave runup from XBSB to the corresponding image from XBNH. The cGAN model achieves improved performance in image-to-image mapping tasks by incorporating physics-based knowledge from XBSB.
  arXiv  Detail & Related papers  (2024-01-12T18:58:37Z)
• Data assimilation and parameter identification for water waves using the nonlinear Schr\"{o}dinger equation and physics-informed neural networks [0.3495246564946556]
  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.
  arXiv  Detail & Related papers  (2024-01-08T07:35:48Z)
• A predictive physics-aware hybrid reduced order model for reacting flows [65.73506571113623]
  A new hybrid predictive Reduced Order Model (ROM) is proposed to solve reacting flow problems. The number of degrees of freedom is reduced from thousands of temporal points to a few POD modes with their corresponding temporal coefficients. Two different deep learning architectures have been tested to predict the temporal coefficients.
  arXiv  Detail & Related papers  (2023-01-24T08:39:20Z)
• Synthetic Wave-Geometric Impulse Responses for Improved Speech Dereverberation [69.1351513309953]
  We show that accurately simulating the low-frequency components of Room Impulse Responses (RIRs) is important to achieving good dereverberation. We demonstrate that speech dereverberation models trained on hybrid synthetic RIRs outperform models trained on RIRs generated by prior geometric ray tracing methods.
  arXiv  Detail & Related papers  (2022-12-10T20:15:23Z)
• Machine Learning model for gas-liquid interface reconstruction in CFD numerical simulations [59.84561168501493]
  The volume of fluid (VoF) method is widely used in multi-phase flow simulations to track and locate the interface between two immiscible fluids. A major bottleneck of the VoF method is the interface reconstruction step due to its high computational cost and low accuracy on unstructured grids. We propose a machine learning enhanced VoF method based on Graph Neural Networks (GNN) to accelerate the interface reconstruction on general unstructured meshes.
  arXiv  Detail & Related papers  (2022-07-12T17:07:46Z)
• An advanced spatio-temporal convolutional recurrent neural network for storm surge predictions [73.4962254843935]
  We study the capability of artificial neural network models to emulate storm surge based on the storm track/size/intensity history. This study presents a neural network model that can predict storm surge, informed by a database of synthetic storm simulations.
  arXiv  Detail & Related papers  (2022-04-18T23:42:18Z)
• Learning Wave Propagation with Attention-Based Convolutional Recurrent Autoencoder Net [0.0]
  We present an end-to-end attention-based convolutional recurrent autoencoder (AB-CRAN) network for data-driven modeling of wave propagation phenomena. We employ a denoising-based convolutional autoencoder from the full-order snapshots given by time-dependent hyperbolic partial differential equations for wave propagation. The attention-based sequence-to-sequence network increases the time-horizon of prediction by five times compared to the plain RNN-LSTM.
  arXiv  Detail & Related papers  (2022-01-17T20:51:59Z)
• Seismic wave propagation and inversion with Neural Operators [7.296366040398878]
  We develop a prototype framework for learning general solutions using a recently developed machine learning paradigm called Neural Operator. A trained Neural Operator can compute a solution in negligible time for any velocity structure or source location. We illustrate the method with the 2D acoustic wave equation and demonstrate the method's applicability to seismic tomography.
  arXiv  Detail & Related papers  (2021-08-11T19:17:39Z)
• Autoencoder-driven Spiral Representation Learning for Gravitational Wave Surrogate Modelling [47.081318079190595]
  We investigate the existence of underlying structures in the empirical coefficients using autoencoders. We design a spiral module with learnable parameters, that is used as the first layer in a neural network, which learns to map the input space to the coefficients. The spiral module is evaluated on multiple neural network architectures and consistently achieves better speed-accuracy trade-off than baseline models.
  arXiv  Detail & Related papers  (2021-07-09T09:03:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.