An Investigation of Stabilization Scaling in Finite-Strain Virtual Element Methods for Hyperelasticity
Paulo Akira F. Enabe, Rodrigo Provasi

TL;DR
This paper introduces a kernel-only stabilization method for finite-strain virtual element methods in hyperelasticity, improving robustness and decoupling volumetric and deviatoric responses, especially near incompressibility.
Contribution
Develops a submesh-free, kernel-only stabilization that decouples volumetric and shear channels, scaling appropriately with the Newton tangent energy and enhancing robustness in nearly incompressible regimes.
Findings
Decoupled stabilization remains shear-scaled as Poisson ratio approaches 0.5.
Spectral analysis confirms uniform equivalence to shear modulus on the kernel.
Numerical tests show improved robustness across polygonal meshes.
Abstract
Low-order virtual element methods (VEM) compute a consistent finite-strain contribution through polynomial projections and rely on stabilization to control the unresolved modes in the projector kernel. In current hyperelastic VEM practice, stabilization is often defined by integrating a nonlinear surrogate energy over an auxiliary sub-triangulation and scaled through modified Lam\'e parameters and incompressibility factors; this can introduce sensitivity to the arbitrary internal tessellation, complicate consistent Newton linearization, and, most critically, inject bulk-dependent proxies into shear-type kernel penalties, artificially stiffening isochoric missing modes in the nearly incompressible regime. This work develops a submesh-free, kernel-only stabilization that decouples deviatoric and volumetric channels and is explicitly designed to scale like the current Newton tangent energy…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsModel Reduction and Neural Networks · Advanced Numerical Methods in Computational Mathematics · Elasticity and Material Modeling
