FNO exhibits strong frequency bias with sharp OOD error growth on high-frequency inputs in wave equations, while DeepONet shows milder degradation despite higher baseline error.
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PhysEDA folds separable Manhattan-distance exponential decay into linear attention and potential-based rewards, cutting complexity to linear while improving zero-shot transfer and sparse-reward performance on decoupling-cap placement, macro placement, and IR-drop prediction.
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Frequency Bias and OOD Generalization in Neural Operators under a Variable-Coefficient Wave Equation
FNO exhibits strong frequency bias with sharp OOD error growth on high-frequency inputs in wave equations, while DeepONet shows milder degradation despite higher baseline error.
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PhysEDA: Physics-Aware Learning Framework for Efficient EDA With Manhattan Distance Decay
PhysEDA folds separable Manhattan-distance exponential decay into linear attention and potential-based rewards, cutting complexity to linear while improving zero-shot transfer and sparse-reward performance on decoupling-cap placement, macro placement, and IR-drop prediction.