SirenFNO: Efficient and Full Frequency Learning of Fourier Neural Operators

arXiv:2606.11518v1 Announce Type: new Abstract: Fourier neural operators (FNOs) are effective and efficient surrogates for approximating solutions of PDEs and generalize across discretizations. However, owing to the reliance on frequency truncation to maintain learning efficiency of FNOs, empirical studies suggest that FNOs exhibit spectral bias toward low-frequency information, which may hinder the learning capability especially for certain PDEs with strong high-frequency oscillations. To address this limitation, we propose SirenFNO, a novel framework that leverages sinusoidal representation

Source: arXiv cs.LG — read the full report at the original publisher.

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