Proximal Galerkin for the isometry constraint

TL;DR

This paper introduces a novel proximal Galerkin numerical method that exactly enforces the isometry constraint in nonlinear plate modeling, overcoming previous challenges and improving efficiency and applicability.

Contribution

The authors develop a proximal Galerkin approach that preserves the isometry constraint exactly without preprocessing, enabling more efficient and broadly applicable simulations of nonlinear plates.

Findings

Method converges to prescribed error tolerance efficiently.

Requires fewer iterations than previous methods.

Works effectively even on coarse meshes.

Abstract

We resolve a longstanding open problem in the computational modeling of nonlinear plates by introducing a numerical method that exactly enforces the isometry constraint, namely, that the first fundamental form of the mid-surface coincides with the identity tensor. Several numerical methods have been proposed to approximate solutions of such manifold-constrained variational problems using gradient flows with tangent space updates. However, this class of methods presents two main challenges. First, a preprocessing step is required to enforce the boundary conditions and generate an initial guess sufficiently close to an isometry. Second, each step of the gradient flow typically increases the isometry defect. We adopt an alternative approach based on the proximal Galerkin framework, originally introduced for variational problems with convex inequality constraints. The resulting method preserves the geometric structure of the feasible set and yields an efficient algorithm in which each iterate is an exact isometry at the barycenter of every mesh cell. In contrast to existing methods, no preprocessing step is required, enabling broader applicability of this important category of mathematical models. Numerical experiments on standard benchmarks demonstrate that the method converges to a prescribed error tolerance in an asymptotically mesh-independent number of iterations and requires substantially fewer iterations than previous methods, even on coarse meshes.