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AdaST introduces an adaptive spatial-temporal forecasting framework that decomposes inputs into components with distinct coupling regimes using heterogeneity-aware experts, then recomposes them with a correlation-informed module to outperform state-of-the-art baselines on traffic, climate, and energy forecasting.
The paper introduces a reinforcement learning-based approach for adaptively selecting full and reduced order models in hybrid domain decomposition simulations, using deep Q-networks to balance accuracy and cost in transient problems.