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

The paper introduces Neural Interpretable PDEs (NIPS), a novel neural operator architecture that enhances nonlocal attention mechanisms for modeling complex physical systems, achieving improved accuracy and efficiency by leveraging Fourier space kernels and linear attention. Empirical results show NIPS outperforms existing methods like NAO across various benchmarks, advancing scalable, interpretable physics learning.