In 1D hard-rod cDFT, Fourier neural operators learn the density-to-direct-correlation-function map more accurately than DeepONet variants and dense networks, with squared ReLU giving the best extrapolation.
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Neural Operators for Forward and Inverse Potential-Density Mappings in Classical Density Functional Theory
In 1D hard-rod cDFT, Fourier neural operators learn the density-to-direct-correlation-function map more accurately than DeepONet variants and dense networks, with squared ReLU giving the best extrapolation.