Physics-Guided Spatiotemporal Learning for Coastal Wave Peak Period Estimation from Video

• URL: http://arxiv.org/abs/2606.13302v1
• Date: Thu, 11 Jun 2026 12:54:35 GMT
• Title: Physics-Guided Spatiotemporal Learning for Coastal Wave Peak Period Estimation from Video
• Abstract summary: The framework combines automated temporal-variance based region-of-interest detection, multi-stage Sim-to-Real transfer learning, and physics-informed regularization.<n>The proposed framework shows the promise of physics-guided video-based deep learning systems for long-term coastal wave monitoring.
• Score: 0.273483625670596
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Wave parameters in the nearshore are crucial for coastal engineering, shoreline protection, marine hazard assessment, and coastal management for climate resilience. Traditional monitoring systems like buoys and radar platforms offer accurate monitoring but can have high installation and maintenance expenses and limited spatial coverage. Passive ocean monitoring using video has been achieved by leveraging deep learning, however, many methods are not physically interpretable, feasible, and validated for oceanography. In thiswork, a Physics-Guided Deep Spatiotemporal Learning Framework for direct estimation of nearshore wave peak periods from passive coastal video stream is proposed. The framework combines automated temporal-variance based region-of-interest detection, multi-stage Sim-to-Real transfer learning, and physics-informed regularization to enhance the predictive accuracy and physical consistency. A variety of spatiotemporal architectures were assessed, such as transformer-based and recurrent-convolutional ones, alongside synthetic pretraining,silver-label adaptation, and expert fine-tuning. The results show that transformer-based architectures outperformed in terms of the accuracy of the instantaneous prediction, while lightweight recurrent-convolutional architectures achieved higher temporal stability and operational oceanographic skill. Ablation studies also demonstrated the benefits of physics-guided regularization in terms of trend-following consistency, and physically implausible predictions. Explainability auditing also helped to focus attention in hydrodynamically active surf-zone regions and showed good agreement with the physically derived wave propagation behavior. In general, the proposed framework shows the promise of physics-guided video-based deep learning systems for long-term coastal wave monitoring that are cost-efficient and operationally feasible.

Related papers

• Multi-Source Dynamic Graph Learning for Compound-Flood Forecasting in Managed Coastal Systems [14.68802969969726]
  Compound flooding in managed coastal systems is influenced by hydrological conditions and water-management activity observed across multiple monitoring stations.<n>Current forecasting models can capture temporal dependencies with low average errors, but global error metrics may conceal poor reproduction of prolonged high-water plateaus.<n>This work proposes an anchored forecasting framework that incorporates cross-site information through state- and lead-dependent bounded residual corrections.
  arXiv  Detail & Related papers  (2026-08-03T06:48:32Z)
• Context-Informed Ship Trajectory Prediction via Conditional Attention [17.738453989249656]
  Long-term ship trajectory prediction is a fundamental capability for maritime safety and autonomous navigation.<n>We propose the Conditional Informer, a novel encoder-decoder architecture that formulates trajectory prediction as a conditional generation task.<n>To address the intermittency of real-world data, we introduce a Modality Masking training strategy to prevent catastrophic degradation during sensor fallback.
  arXiv  Detail & Related papers  (2026-07-29T19:38:55Z)
• SkyJEPA: Learning Long-Horizon World Models for Zero-Shot Sim-to-Real Control of Quadrotors [48.60897189520386]
  We introduce the JEPA-style model for real-time quadrotor control.<n>We combine a latent dynamics model with a novel physics-inspired prober that maps frozen latents to interpretable state.<n>To reduce the dependence on expensive and unsafe real-world data collection, we develop a structured pipeline for automated dataset generation.
  arXiv  Detail & Related papers  (2026-06-22T15:00:59Z)
• Predictive Modeling of Maritime Radar Data Using Transformer Architecture [1.0116530711210054]
  transformer architectures have revolutionized AIS-based trajectory prediction and demonstrated feasibility for sonar frame forecasting.<n>Our review shows that, while the literature has demonstrated transformer-based frame prediction for sonar sensing, no prior work addresses transformer-based maritime radar frame prediction.
  arXiv  Detail & Related papers  (2025-12-18T21:52:43Z)
• Observation-driven correction of numerical weather prediction for marine winds [14.58447529631976]
  We reformulate wind forecasting as observation-informed correction of a global numerical weather prediction (NWP) model.<n>We learn local correction patterns by assimilating the latest in-situ observations to adjust the Global Forecast System (GFS) output.<n>Model reduces GFS 10-meter wind RMSE at all lead times up to 48 hours, achieving 45% improvement at 1-hour lead time and 13% improvement at 48-hour lead time.
  arXiv  Detail & Related papers  (2025-12-03T09:39:44Z)
• A Physics Prior-Guided Dual-Stream Attention Network for Motion Prediction of Elastic Bragg Breakwaters [7.146484689550911]
  Conventional deep learning models often exhibit limited generalization capabilities when presented with unseen sea states.<n>This study proposes a novel Physics Prior-Guided Dual-Stream Attention Network (PhysAttnNet)<n>Experiments on wave flume datasets demonstrate that PhysAttnNet significantly outperforms mainstream models.
  arXiv  Detail & Related papers  (2025-10-16T03:06:44Z)
• Forecasting Seismic Waveforms: A Deep Learning Approach for Einstein Telescope [46.4942794596355]
  We introduce textitSeismoGPT, a transformer-based model for forecasting three-component seismic waveforms in the context of future gravitational wave detectors like the Einstein Telescope.<n>By learning temporal and spatial dependencies directly from waveform data, SeismoGPT captures realistic ground motion patterns and provides accurate short-term forecasts.
  arXiv  Detail & Related papers  (2025-09-25T19:16:13Z)
• Storm Surge in Color: RGB-Encoded Physics-Aware Deep Learning for Storm Surge Forecasting [0.5242869847419834]
  Storm surge forecasting plays a crucial role in coastal disaster preparedness, yet existing machine learning approaches often suffer from limited spatial adaptability.<n>In this work, we introduce a novel approach that projects unstructured water elevation fields onto structured Red Green (RGB)-encoded representations.<n>Our method demonstrates robust 48-hour forecasting performance across multiple regions along the Texas coast and exhibits strong spatial adaptability to other coastal areas.
  arXiv  Detail & Related papers  (2025-06-26T19:56:30Z)
• Multivariate Long-term Time Series Forecasting with Fourier Neural Filter [42.60778405812048]
  We introduce FNF as the backbone and DBD as architecture to provide excellent learning capabilities and optimal learning pathways for spatial-temporal modeling.<n>We show that FNF unifies local time-domain and global frequency-domain information processing within a single backbone that extends naturally to spatial modeling.
  arXiv  Detail & Related papers  (2025-06-10T18:40:20Z)
• Physics-guided Active Sample Reweighting for Urban Flow Prediction [75.24539704456791]
  Urban flow prediction is a nuanced-temporal modeling that estimates the throughput of transportation services like buses, taxis and ride-driven models. Some recent prediction solutions bring remedies with the notion of physics-guided machine learning (PGML) We develop a atized physics-guided network (PN), and propose a data-aware framework Physics-guided Active Sample Reweighting (P-GASR)
  arXiv  Detail & Related papers  (2024-07-18T15:44:23Z)
• Machine learning for phase-resolved reconstruction of nonlinear ocean wave surface elevations from sparse remote sensing data [37.69303106863453]
  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.
  arXiv  Detail & Related papers  (2023-05-18T12:30:26Z)
• Learning-based estimation of in-situ wind speed from underwater acoustics [58.293528982012255]
  We introduce a deep learning approach for the retrieval of wind speed time series from underwater acoustics. Our approach bridges data assimilation and learning-based frameworks to benefit both from prior physical knowledge and computational efficiency.
  arXiv  Detail & Related papers  (2022-08-18T15:27:40Z)
• Physics-Inspired Temporal Learning of Quadrotor Dynamics for Accurate Model Predictive Trajectory Tracking [76.27433308688592]
  Accurately modeling quadrotor's system dynamics is critical for guaranteeing agile, safe, and stable navigation. We present a novel Physics-Inspired Temporal Convolutional Network (PI-TCN) approach to learning quadrotor's system dynamics purely from robot experience. Our approach combines the expressive power of sparse temporal convolutions and dense feed-forward connections to make accurate system predictions.
  arXiv  Detail & Related papers  (2022-06-07T13:51:35Z)
• Forecasting large-scale circulation regimes using deformable convolutional neural networks and global spatiotemporal climate data [86.1450118623908]
  We investigate a supervised machine learning approach based on deformable convolutional neural networks (deCNNs) We forecast the North Atlantic-European weather regimes during extended boreal winter for 1 to 15 days into the future. Due to its wider field of view, we also observe deCNN achieving considerably better performance than regular convolutional neural networks at lead times beyond 5-6 days.
  arXiv  Detail & Related papers  (2022-02-10T11:37:00Z)

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.