Fourier Neural Operator
A neural operator model proposed by Zongyi Li et al. in 2020 that learns operator mappings in the frequency domain to solve partial differential equations. The architecture is characterized by the ability to capture long-range dependencies and exhibits resolution invariance.
Timeline (last 90 days)
The paper's system consists of four modules: a global Fourier Neural Operator, a local operator, a set-aware selector, and a macro policy.
FNO transforms the input function to frequency space via Fast Fourier Transform, applies a learnable linear transformation to the low-frequency components, and then transforms back to the spatial domain via inverse FFT.
FNO has resolution invariance: it can be trained on low-resolution grids and directly transferred to finer grids at inference time.
All Facts (3)
The paper's system consists of four modules: a global Fourier Neural Operator, a local operator, a set-aware selector, and a macro policy.
50%UnverifiedFNO transforms the input function to frequency space via Fast Fourier Transform, applies a learnable linear transformation to the low-frequency components, and then transforms back to the spatial domain via inverse FFT.
50%UnverifiedFNO has resolution invariance: it can be trained on low-resolution grids and directly transferred to finer grids at inference time.
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