Complex frequency-domain feature learning network for phase-resolved wave and ship motion prediction
Authors:
- Limin HuangLHLimin Huang
- Gaoxiang SunGSGaoxiang Sun
- Hangyu ChenHCHangyu Chen
- Mao LiMLMao Li
- Xuewen MaXMXuewen Ma
- 19 May 2026
- journal article
- Published by Elsevier in Engineering Applications of Artificial Intelligence
- Vol. 178
Abstract
No abstract availableFunding Information
- National Natural Science Foundation of China (52471361)
- National Natural Science Foundation of China (52401419)
This publication has 34 references indexed in Scilit:
- Hierarchical reinforcement learning for dynamic collision avoidance of autonomous ships under uncertain scenariosKnowledge-Based Systems, 2025
- A novel framework for significant wave height prediction based on Adaptive Feature Extraction Time-Frequency NetworkEnergy, 2025
- Predicting heave and pitch motions of an FPSO using meta-learningMarine Structures, 2024
- Hierarchical attention-guided multiscale aggregation network for infrared small target detectionNeural Networks, 2023
- Surface similarity parameter: A new machine learning loss metric for oscillatory spatio-temporal dataNeural Networks, 2022
- Wave runup prediction for a semi-submersible based on temporal convolutional neural networkJournal of Ocean Engineering and Science, 2022
- Time-series forecasting for ships maneuvering in waves via recurrent-type neural networksJournal of Ocean Engineering and Marine Energy, 2022
- Data-driven models for vessel motion prediction and the benefits of physics-based informationApplied Ocean Research, 2022
- Phase-resolved wave prediction model for long-crest waves based on machine learningComputer Methods in Applied Mechanics and Engineering, 2020
- Machine learning-based wear fault diagnosis for marine diesel engine by fusing multiple data-driven modelsKnowledge-Based Systems, 2020