5 citations · 5 across the 3 of their papers we have counts for
3 papers
physics.ao-ph2026
On the Limits of Univariate Deep Learning for Significant Wave Height Forecasting
Yilin Zhai, Hongyuan Shi, Zaijin You
This study conducts a systematic hyperparameter search across five deep learning architectures, DLinear, LSTM, PatchTST, ResAttLstm, and Mamba2, and nine context lengths (1-168 h)…
eess.SP2025
AUWave: A Data-Driven Model for Reconstructing Significant Wave Heights Using Sparse Observations
Hongyuan Shi, Yilin Zhai, Ping Dong +3
Reconstructing high-resolution regional significant wave height (SWH) fields from sparse buoy observations is a critical challenge for ocean monitoring. We introduce AUWave, a hybr…
cs.LG2025★ 5 cited
Improving Significant Wave Height Prediction Using Chronos Models
Yilin Zhai, Hongyuan Shi, Chao Zhan +3
Accurate wave height prediction is critical for maritime safety and coastal resilience, yet conventional physics-based models and traditional machine learning methods face challeng…