Replace pointwise fractional derivatives of noisy trajectories in a neural PDE or neural dynamics loss with weak projections in which the fractional operator acts on smooth test functions. The network is trained to match integral residuals over local space-time windows, making the residual insensitive to high-frequency measurement noise while retaining sensitivity to the underlying fractional dynamics.
Train a neural field to represent a sphere-valued phase or feature map with a prescribed codimension-two defect set. Add a fractional Sobolev energy to suppress high-frequency oscillations, but enforce topology through a discrete Jacobian or winding-current loss so that smoothing cannot remove holes, filaments, or vortex defects.