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This paper introduces SCOPE-Bench, a benchmark for evaluating molecular out-of-distribution generalization, and POMA, a framework using reinforcement learning to select source domains for domain adaptation, achieving significant error reductions on 3D molecular models.
This paper investigates the generalization behavior of Fourier Neural Operators and Deep Operator Networks under distribution shifts in a variable-coefficient wave equation, revealing that FNO struggles with high-frequency inputs while DeepONet shows milder degradation.