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OceanMoE introduces a Mixture-of-Experts framework with structured conditional sparse computation to balance shared ocean context and adaptive specialization for multivariate ocean forecasting, demonstrating improved accuracy in long-horizon predictions.
Rollout-Decoded Reconstruction (RDR) is a loss term that improves long-horizon prediction in latent world models by free-running the model during training, achieving a 1.80× improvement in valid prediction time on chaotic systems.
Presents GeoIncNO, a geometry-aware incremental neural operator that improves long-horizon PDE prediction via residual latent increments and mean-fluctuation decoupled reconstruction, achieving better stability and spectral fidelity on 1D/2D/3D benchmarks.
This paper introduces attention-free latent memory and dynamic re-encoding to improve long-horizon predictions in Koopman autoencoders, reducing error accumulation on benchmark dynamical systems.