What question did this study set out to answer?

The aim is to enhance the efficiency and speed of Fourier Neural Operators (FNO) used for solving partial differential equations (PDEs).

FNO-Speed: Accelerate Fourier Neural Operators Training for Solving PDEs

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Randomized trial enhances computational efficiency and training speed for Fourier Neural Operators in solving PDEs.

The aim is to enhance the efficiency and speed of Fourier Neural Operators (FNO) used for solving partial differential equations (PDEs).
Integration of multi-level parallel FNO-aware mapping with GEMM optimization.
Implementation of custom-sized high-frequency signal filtering.
Adaptation of data reorganization and computation restructuring for optimized memory access.
Achieved up to 1.4 × end-to-end training speedup.
Parallel efficiency reached approximately 70% on 4 GPUs.
Maintained nearly identical accuracy compared to traditional FNO methods.

Song et al. (2026) studied this question.

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