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Improving Extreme Wind Prediction with Frequency-Informed Learning

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
Extreme Weather ForecastingMeteorological AnalysisAI for Science
Abstract:

Accurate prediction of extreme wind velocities has substantial significance in industry, particularly for the operation management of wind power plants. Although the state-of-the-art data-driven models perform well for general meteorological forecasting, they may exhibit large errors for extreme weather—for example, systematically underestimating the magnitudes and short-term variation of extreme winds. To address this issue, we conduct a theoretical analysis of how the data frequency spectrum influences errors in extreme wind prediction. Based on these insights, we propose a novel loss function that incorporates a gradient penalty to mitigate the magnitude shrinkage of extreme weather, and we theoretically justify its effectiveness via a PDE-based energy–enstrophy analysis. To capture more precise short-term wind velocity variations, we design a novel structure of physics-embedded machine learning models with frequency reweighting. Experiments demonstrate that, compared to the baseline models, our approach achieves significant improvements in predicting extreme wind velocities while maintaining robust overall performance.

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This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper proposes a frequency-informed learning framework for extreme wind velocity prediction, combining theoretical analysis of amplitude shrinkage with a gradient-penalized loss function and a physics-embedded neural architecture. It resides in the 'Physics-Informed Learning for Extreme Wind Prediction' leaf, which contains only one sibling paper among the 28 total papers in the taxonomy. This positioning indicates a relatively sparse research direction within the broader field of extreme wind forecasting, suggesting the work addresses a niche but important gap where physics-based constraints meet frequency-domain modeling for extreme events.

The taxonomy reveals that neighboring research directions pursue different modeling philosophies. The 'Frequency-Domain Decomposition and Multi-Scale Modeling' branch emphasizes wavelet and empirical mode decomposition techniques, while 'Fourier and Attention-Based Frequency Modeling' focuses on transformer architectures with spectral enhancements. The paper's approach bridges these areas by incorporating frequency reweighting within a physics-embedded framework, diverging from pure decomposition methods and pure attention mechanisms. The 'Climate-Informed Extreme Weather Forecasting' and 'Statistical Extreme Value Modeling' leaves represent alternative paradigms for handling extremes, focusing on climate projections and statistical distributions rather than frequency-domain neural architectures.

Among 22 candidates examined across three contributions, the gradient-penalized loss function shows one refutable candidate out of 10 examined, while the frequency-domain theoretical analysis (0 of 4) and physics-embedded architecture (0 of 8) appear more novel within this limited search scope. The contribution-level statistics suggest that the loss function design has more substantial prior work overlap, whereas the theoretical analysis of amplitude shrinkage and the specific neural architecture design face less direct competition. However, the search scope is constrained to top-K semantic matches and citation expansion, not an exhaustive literature review.

Based on the limited search of 22 candidates, the work appears to occupy a relatively underexplored intersection of frequency-domain analysis, physics-informed constraints, and extreme event prediction. The sparse taxonomy leaf and low refutation rates suggest novelty, though the analysis cannot rule out relevant prior work outside the examined candidate set. The framework's combination of theoretical justification via PDE-based analysis and practical neural architecture design distinguishes it from neighboring approaches that emphasize either decomposition techniques or pure data-driven learning.

Taxonomy

Core-task Taxonomy Papers
28
3
Claimed Contributions
22
Contribution Candidate Papers Compared
1
Refutable Paper

Research Landscape Overview

Core task: Extreme wind velocity prediction with frequency-informed learning. The field of extreme wind prediction has evolved into several interconnected branches that reflect different modeling philosophies and problem settings. Frequency-Domain Decomposition and Multi-Scale Modeling approaches emphasize breaking down wind signals into constituent frequency components, often using wavelet transforms or similar techniques to capture both slow-varying trends and rapid fluctuations. Fourier and Attention-Based Frequency Modeling leverages spectral representations and transformer-style architectures to learn frequency-aware patterns directly from data, as seen in works like A novel GRU-Informer hybrid[1] and Fast-Powerformer[6]. Extreme Event Prediction and Loss Function Design focuses on the challenge of accurately forecasting rare, high-impact wind events, where standard loss functions may underweight tail behavior. Finally, Spatial and Spatio-Temporal Wind Field Modeling addresses the reconstruction and forecasting of wind fields across geographic regions, integrating both temporal dynamics and spatial dependencies. Within the Extreme Event Prediction branch, a central theme is how to balance general forecasting accuracy with the ability to capture infrequent but critical wind extremes. Improving Extreme Wind Prediction[0] sits squarely in this area, specifically within the Physics-Informed Learning for Extreme Wind Prediction cluster, where it emphasizes frequency-informed techniques to better resolve extreme events. This contrasts with purely data-driven methods that may overlook physical constraints or frequency characteristics. Nearby work such as Fdmnet[27] also explores frequency decomposition mechanisms, suggesting a shared interest in multi-scale signal processing. The trade-off between incorporating domain knowledge—such as spectral properties of turbulent wind—and maintaining model flexibility remains an open question, with some studies favoring hybrid architectures that blend classical signal processing with modern deep learning, while others pursue end-to-end learned representations.

Claimed Contributions

Frequency-domain theoretical analysis of amplitude shrinkage in extreme wind prediction

The authors provide a Fourier-domain decomposition showing that small spatial shifts and scaling yield wavenumber-dependent MSE, theoretically explaining why standard MSE training causes underestimation of extreme wind amplitudes and smearing of short-term variability.

4 retrieved papers
Gradient-penalized loss function with energy–enstrophy interpretation

The authors introduce a loss function augmenting MSE with a gradient-matching term that reweights high-frequency errors. They provide a PDE-based energy–enstrophy interpretation showing this loss enforces enstrophy matching and controls small-scale vorticity, mitigating amplitude shrinkage.

10 retrieved papers
Can Refute
Physics-embedded neural architecture with frequency separation and reweighting

The authors design a neural framework combining a Navier-Stokes operator backbone with frequency separation (Fourier masking into high- and low-frequency bands) and learnable reweighting to capture precise short-term wind velocity variations while maintaining stability and parameter efficiency.

8 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Frequency-domain theoretical analysis of amplitude shrinkage in extreme wind prediction

The authors provide a Fourier-domain decomposition showing that small spatial shifts and scaling yield wavenumber-dependent MSE, theoretically explaining why standard MSE training causes underestimation of extreme wind amplitudes and smearing of short-term variability.

Contribution

Gradient-penalized loss function with energy–enstrophy interpretation

The authors introduce a loss function augmenting MSE with a gradient-matching term that reweights high-frequency errors. They provide a PDE-based energy–enstrophy interpretation showing this loss enforces enstrophy matching and controls small-scale vorticity, mitigating amplitude shrinkage.

Contribution

Physics-embedded neural architecture with frequency separation and reweighting

The authors design a neural framework combining a Navier-Stokes operator backbone with frequency separation (Fourier masking into high- and low-frequency bands) and learnable reweighting to capture precise short-term wind velocity variations while maintaining stability and parameter efficiency.

Table of Contents

Improving Extreme Wind Prediction with Frequency-Informed Learning | Novelty Validation