Add a sparse, interpretable fractional-dynamics layer to a neural world model: candidate terms are evaluated through weak projections, while both their support and continuous derivative orders are selected by validation error versus model complexity. This avoids forcing the model to choose from a dense fixed dictionary containing many nearly collinear fractional orders.
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.