June 11, 2024, 4:48 a.m. | Miguel Liu-Schiaffini, Julius Berner, Boris Bonev, Thorsten Kurth, Kamyar Azizzadenesheli, Anima Anandkumar

cs.LG updates on arXiv.org arxiv.org

arXiv:2402.16845v2 Announce Type: replace
Abstract: Neural operators learn mappings between function spaces, which is practical for learning solution operators of PDEs and other scientific modeling applications. Among them, the Fourier neural operator (FNO) is a popular architecture that performs global convolutions in the Fourier space. However, such global operations are often prone to over-smoothing and may fail to capture local details. In contrast, convolutional neural networks (CNN) can capture local features but are limited to training and inference at a …

abstract applications architecture arxiv cs.ai cs.lg cs.na differential fourier function global however integral learn math.na modeling operations operators popular practical replace scientific solution space spaces them type

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