E2E-FANet: A Highly Generalizable Framework for Waves prediction Behind Floating Breakwaters via Exogenous-to-Endogenous Variable Attention (2505.06690v1)

Abstract: Accurate prediction of waves behind floating breakwaters (FB) is crucial for optimizing coastal engineering structures, enhancing safety, and improving design efficiency. Existing methods demonstrate limitations in capturing nonlinear interactions between waves and structures, while exhibiting insufficient capability in modeling the complex frequency-domain relationships among elevations of different wave gauges. To address these challenges, this study introduces the Exogenous-to-Endogenous Frequency-Aware Network (E2E-FANet), a novel end-to-end neural network designed to model relationships between waves and structures. The E2E-FANetarchitecture incorporates a Dual-Basis Frequency Mapping (DBFM) module that leverages orthogonal cosine and sine bases to extract wave features from the frequency domain while preserving temporal information. Additionally, we introduce the Exogenous-to-Endogenous Cross-Attention (E2ECA) module, which employs cross attention to model the interactions between endogenous and exogenous variables. We incorporate a Temporal-wise Attention (TA) mechanism that adaptively captures complex dependencies in endogenous variables. These integrated modules function synergistically, enabling E2E-FANet to achieve both comprehensive feature perception in the time-frequency domain and precise modeling of wave-structure interactions. To comprehensively evaluate the performance of E2E-FANet, we constructed a multi-level validation framework comprising three distinct testing scenarios: internal validation under identical wave conditions, generalization testing across different wave conditions, and adaptability testing with varying relative water density (RW) conditions. These comprehensive tests demonstrate that E2E-FANet provides accurate waves behind FB predictions while successfully generalizing diverse wave conditions.

An Analysis of E2E-FANet for Wave Prediction Behind Floating Breakwaters

This essay provides an analytical overview of the paper titled "E2E-FANet: A Highly Generalizable Framework for Waves prediction Behind Floating Breakwaters via Exogenous-to-Endogenous Variable Attention" by Zhang Jianxin et al. This research introduces E2E-FANet, a novel deep learning framework specifically designed to address the complexities inherent in predicting wave patterns behind floating breakwaters (FB).

The paper addresses the limitations of traditional wave prediction models, such as their inadequate handling of the nonlinear interactions and complex frequency-domain relationships responsible for wave propagation behind FB in mixed wave environments. The research introduces E2E-FANet, a framework integrating advanced neural network components to improve wave prediction accuracy and generalization capabilities for coastal engineering applications.

Core Contributions and Methodology

The paper identifies several key contributions of the E2E-FANet framework:

1. Architecture Innovations: The paper employs a Dual-Basis Frequency Mapping (DBFM) module coupled with an Exogenous-to-Endogenous Cross-Attention (E2ECA) module. The combination of these features facilitates the extraction of comprehensive frequency-domain wave features, while simultaneously modeling the causality between exogenous variables (e.g., motion responses of the floating body) and endogenous variables (e.g., wave elevations).
2. Modular Design: E2E-FANet's architecture is composed of several synergistic modules:
   • Embedding Layer: Converts raw wave data into feature vectors.
   • DBFM Module: Utilizes Discrete Fourier Transform (DFT) to decompose wave signals into cosine and sine basis functions, ensuring a comprehensive frequency analysis.
   • Temporal-wise Attention (TA): Focuses on adapting the prediction to critical temporal patterns and dependencies over time.
   • E2ECA Mechanism: Models the interaction between exogenous and endogenous variables through sophisticated cross-attention processes.
3. Robust Evaluation: The authors validated the framework across various testing scenarios to optimize its performance, including internal tests under identical wave conditions, adaptability to different relative water density conditions, and generalization over diverse wave scenarios.

Strong Numerical Results and Baseline Comparisons

E2E-FANet's efficacy was established through rigorous comparative assessments against conventional architectures, such as LSTM, CNN-LSTM, and several Transformer-based models. Key numerical achievements include reducing Mean Squared Error (MSE) by 19.95%, and enhancing overall generalization capabilities, significantly outperforming comparative models.

Implications and Future Directions

Practically, the E2E-FANet showcases potential for deployment in applications requiring accurate real-time wave forecasts, crucial for coastal infrastructure's safety and operational efficiency. Theoretically, this research advances the field of wave prediction by facilitating a comprehensive understanding of complex wave dynamics via advanced neural network architectures.

Future research should aim at addressing E2E-FANet's real-time application limitations due to its computational complexity. Efforts can be directed towards improving model interpretability and validation on larger datasets from real-world environments, potentially incorporating oceanographic data to ensure robustness across varied marine conditions.

Overall, the proposed E2E-FANet framework presents a significant stride in the accurate modeling of wave-structure interactions, offering a sophisticated and reliable tool for advancing coastal and marine engineering paradigms.