arXiv AI
Read, Write, Relax: Why Neural PDE Surrogates Need Both Global and Local Processing
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MeshTok: Efficient Multi-Scale Tokenization for Scalable PDE Transformers
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Disentangling Attention in Deep Operator Learning: A Controlled Study of Data-Driven and Physics-Informed Architectures
The paper investigates how different attention mechanisms affect the performance of DeepONet neural operators. Five variants—varying in cross‑attention, self‑attention, tokenization, and attention depth—are trained in both data‑driven and physics‑informed settings on one‑ and two‑dimensional PDE benchmarks. Results show that per‑sensor tokenization with cross‑attention consistently reduces error, while branch self‑attention helps only in complex spatial problems, and deeper cross‑attention yields diminishing returns with higher cost.
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Language Models Can Control Their Own Attention
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arXiv AI
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