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arXiv Machine Learning By Yishun Zhu, Jian Wang

OceanMoE: Structured Conditional Sparse Computation for Long-Horizon Multivariate Ocean Forecasting

Read the original on arXiv Machine Learning →

OceanMoE is a structured conditional sparse Mixture-of-Experts framework designed for long‑horizon multivariate ocean forecasting. It fuses cross‑variable information to build target‑specific local representations and performs content‑conditioned sparse routing at each spatial location, with the number of active experts adjusted by router confidence. Experiments on ORAS5 data show that OceanMoE reduces aggregate forecasting error and maintains lower geometric‑mean normalized RMSE compared to baselines, while expert allocation varies with prediction targets and locations.

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