Disentangling attention in deep operator learning: a controlled study comparing data-driven and physics-informed architectures
Read the original at arxiv.org→arXiv:2609.04407v1 Announce Type: new Abstract: Deep neural operators learn mappings between input functions and complete PDE solution fields, enabling forward evaluations of new problem instances orders of...
Original headline: "Disentangling Attention in Deep Operator Learning: A Controlled Study of Data-Driven and Physics-Informed Architectures"
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- Sep 7, 04:00 UTC arXiv cs.LG lead source Disentangling Attention in Deep Operator Learning: A Controlled Study of Data-Driven and Physics-Informed Architectures