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In summary, Fourier Embeddings serve as a principled mechanism for spec- tral lifting, enabling DeepONet to perform a learned Galerkin-style decom- position of operators

The operator learning framework parallels well-established numerical discretization techniques, such as Petrov-Galerkin, spectral methods, but operates in a fully data-driven

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FEDONet : Fourier-Embedded DeepONet for Spectrally Accurate Operator Learning

cs.LG · 2025-09-15 · conditional · novelty 5.0

FEDONet augments DeepONet with Fourier-embedded trunk networks using random Fourier features, yielding lower L2 reconstruction errors than standard DeepONet on Burgers', 2D Poisson, Eikonal, Allen-Cahn, and Kuramoto-Sivashinsky equations across dataset sizes and noise levels.

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  • FEDONet : Fourier-Embedded DeepONet for Spectrally Accurate Operator Learning cs.LG · 2025-09-15 · conditional · none · ref 56

    FEDONet augments DeepONet with Fourier-embedded trunk networks using random Fourier features, yielding lower L2 reconstruction errors than standard DeepONet on Burgers', 2D Poisson, Eikonal, Allen-Cahn, and Kuramoto-Sivashinsky equations across dataset sizes and noise levels.