Proximal Galerkin for the isometry constraint
It solves a longstanding open problem in computational modeling of nonlinear plates by providing an efficient, constraint-exact method that broadens applicability.
This paper introduces a numerical method that exactly enforces the isometry constraint for nonlinear plate models, eliminating the need for preprocessing and achieving convergence in an asymptotically mesh-independent number of iterations with substantially fewer iterations than previous methods.
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.