跨材料刚度与几何的软组织形变与受力预测模型，实现高精度实时仿真。
Generalizing Soft Tissue Deformation and Force Prediction Across Material Stiffness and Geometry
- 基于校准的超弹性模型构建训练数据，使用软度条件不变图网络建模
- 形变预测均方误差小于1毫米，推理仅需0.010秒，支持未见几何结构
- 强调上游建模校准一致性对受力预测质量的关键影响，适合外科模拟应用
精确的软组织仿真对术前规划、手术训练和触觉反馈系统至关重要。基于学习的代理模型虽能实现实时推理，但其可靠性依赖于准确的本构模型校准。现有方法缺乏在不同刚度水平下模型选择的系统性指导，且难以跨刚度或几何泛化。本文在SOFA框架中对多种刚度的重力加载硅胶梁进行超弹性本构模型的全面校准，利用校准后的仿真数据训练一种软度条件不变的图神经网络，实现跨多种组织类型和未知几何的形变与受力预测。模型在0.010秒内达到亚毫米级平均形变精度，且表明受力预测质量直接依赖于上游校准的一致性。
原文摘要 · Abstract (English)
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 data, we use a softness conditioned equivariant graph neural network, enabling deformation and force prediction across multiple tissue types and unseen geometries. Our model achieves sub-millimeter mean deformation accuracy at 0.010s inference time, while showing that force prediction quality is directly tied to upstream calibration consistency.
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