OceanMoE: Conditional Sparse MoE Cuts Error in Long-Horizon Ocean Forecasting
burny_tech · x · 2026-09-21
A new arXiv paper proposes OceanMoE, a structured conditional sparse Mixture-of-Experts framework for long-horizon multivariate ocean forecasting.
The core idea balances cross-variable sharing with target-specific specialization in a unified model:
- Fuses cross-variable information into target-specific local representations and performs content-conditioned sparse routing at each spatial location, with the number of active experts adapting to router confidence.
- Decoder routing adds a learned geographic bias parameterized by spherical-harmonic spatial bases.
- Shared residual and seasonal pathways supply common cross-variable and month-dependent context.
On long-horizon autoregressive ORAS5 forecasting, OceanMoE lowers aggregate error in both evaluated settings and keeps lower geometric-mean normalized RMSE than the corresponding baselines.
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