Adding Fourier features to the input layer lets the Deep Ritz Method generate high-frequency solutions for non-convex multi-well energy problems, while increasing network depth alone does not consistently do so.
Dacorogna, Direct methods in the calculus of variations, V ol
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Deep Ritz method with Fourier feature mapping: A deep learning approach for solving variational models of microstructure
Adding Fourier features to the input layer lets the Deep Ritz Method generate high-frequency solutions for non-convex multi-well energy problems, while increasing network depth alone does not consistently do so.