Generalizing Soft Tissue Deformation and Force Prediction Across Material Stiffness and Geometry
Accurate soft tissue simulation is essential for surgical training, pre-operative planning, and haptic feedback systems. While learning-based surrogate models trained on data using the finite element method (FEM) offer a promising path to real-time inference, their reliability depends on well-calibrated constitutive models. Existing approaches neither provide systematic guidance on model selection across stiffness levels, nor generalize across different tissue stiffnesses or geometries. We perform a comprehensive calibration of hyperelastic constitutive models in the SOFA Framework using gravity-loaded silicone beams with different stiffnesses. Using calibrated simulations as training dat...
| Authors | Madina Kojanazarova, Sidaty El Hadramy, Philippe C. Cattin |
|---|---|
| Primary category | cs.AI |
| Published | 21 August 2026 |
| Last updated at source | 21 August 2026 |
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