HyperShape: Hyperelasticity Across Diverse Shapes
Hyperelastic deformations are highly sensitive to domain geometry and boundary conditions, making generalization across both a critical capability for neural operators applied to these problems. However, existing benchmarks for neural operators on hyperelasticity rely on simple or few geometries, which makes it difficult to assess this capability rigorously. To address this gap, we introduce HyperShape, an extensible framework designed to generate synthetic shapes and their corresponding hyperelastic simulation data, producing a suite of 2D and 3D datasets with adjustable complexity and controllable shape variations. This design enables systematic assessment of generalization across in-di...
| Authors | Leo Widmer, Sidaty El Hadramy, Stéphane Cotin et al. |
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| Primary category | cs.CE |
| Published | 2 June 2026 |
| Last updated at source | 2 June 2026 |
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