Beignet neural field architecture targets PDEs and singularities

A post introduces beignet as a new neural field architecture that uses a multiresolution Fourier frequency pyramid. Its author claims faster convergence and higher precision when solving partial differential equations (PDEs) and finding singularities, and describes a teaser video as showing beignet fitting an image “super fast.”