<ul><li>Attention mechanisms are powerful tools used in AI, and their potential for modeling complex physical systems is being explored in a new study.</li><li>A new neural operator architecture called Neural Interpretable PDEs (NIPS) enhances Nonlocal Attention Operators (NAO) for better predictive accuracy and computational efficiency in solving inverse PDE problems.</li><li>NIPS employs a linear attention mechanism and introduces a learnable kernel network for scalable learning, eliminating the need for large pairwise interactions and improving spatial interaction handling through Fourier transform.</li><li>Empirical evaluations show that NIPS outperforms NAO and other baselines on various benchmarks, marking a significant advancement in scalable, interpretable, and efficient physics learning.</li></ul>

Neural Interpretable PDEs: Harmonizing Fourier Insights with Attention for Scalable and Interpretable Physics Discovery

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