Neural Representation of Minimal Surfaces
Jiayin Sun, Albert Chern
cs.GR cs.LG
Abstract
We propose a neural representation for minimal surfaces. Unlike prior approaches based on discretization or Physics-Informed Neural Networks (PINNs), where meshes or neural fields are optimized to approximate the governing equations, our method builds on an exact representation, similar to the classical Weierstrass--Enneper parameterization, yielding minimal surfaces up to negligible quadrature error in evaluation. We formulate a training objective for the Plateau problem that optimizes over this representation.
Topics
Classified with taxonomy v2 on Sat, 5 Sept 2026.