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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.
Proposes a hierarchical attention mechanism using overlapping Schwarz domain decomposition to replace dense global low-rank attention with a two-level additive structure of local and coarse blocks, showing faster training and better accuracy with fewer parameters.