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arXiv AI By Madina Kojanazarova, Sidaty El Hadramy, Philippe C. Cattin

Generalizing Soft Tissue Deformation and Force Prediction Across Material Stiffness and Geometry

Read the original on arXiv AI →

The paper presents a method for accurately simulating soft tissue deformation and predicting forces across varying material stiffnesses and geometries. It calibrates hyperelastic constitutive models in the SOFA Framework using gravity‑loaded silicone beams, then trains a softness‑conditioned equivariant graph neural network on the calibrated simulations. The resulting model achieves sub‑millimeter deformation accuracy with 0.010 s inference time, and demonstrates that force prediction quality depends on consistent upstream calibration.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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