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cs.LG updates on arXiv.org

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Discrete Elastic Ribbons: A Unified Discrete Differential...
Shivam Kumar · 2026-05-08 · via cs.LG updates on arXiv.org

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Abstract:Elastic ribbons, slender structures whose length ($L$), width ($W$), and thickness ($b$) satisfy $L \gg W \gg b$, exhibit mechanical behaviors intermediate between one-dimensional rods ($L \gg W, b$) and two-dimensional plates ($L, W \gg b$). In quadratic Kirchhoff-type rod-based frameworks, such as Discrete Elastic Rods (DER), the governing equilibrium equations are independent of width, and therefore these models cannot capture width-dependent mechanical effects. Reduced centerline-based ribbon models attempt to capture width dependence via coupled bending-twisting energies. However, their relative accuracy remain unclear due to the absence of a unified simulation framework. In this work, we formulate a framework grounded in discrete differential geometry where the energy is expressed as functions of coupled bending-twisting strain measures along the centerline, rather than a linear sum of quadratic bending and twisting energies in DER. We derive analytical gradients and Hessians of the energy that enable implicit time integration. Within this unified setting, we compare five ribbon models: Kirchhoff, Sadowsky, Wunderlich, Sano, and Audoly. As a benchmark, a straight ribbon is longitudinally constrained into a pre-buckled arch and subjected to transverse displacement, inducing a supercritical pitchfork bifurcation. Predicted bifurcation thresholds are compared against shell-based finite element simulations, with the Sano model providing the closest agreement in capturing width-dependent shifts. Our high-performance JAX-based implementation achieves $\mathcal{O}(N)$ per-iteration cost and also confirms that Sano model introduces negligible per-iteration overhead relative to standard DER.
Comments: 59 pages, 9 figures, 5 tables. Source code available on this https URL and this https URL
Subjects: Computational Engineering, Finance, and Science (cs.CE); Graphics (cs.GR); Machine Learning (cs.LG)
Cite as: arXiv:2605.05529 [cs.CE]
  (or arXiv:2605.05529v1 [cs.CE] for this version)
  https://doi.org/10.48550/arXiv.2605.05529

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Shivam Kumar Panda [view email]
[v1] Thu, 7 May 2026 00:14:32 UTC (10,928 KB)