跨材料刚度与几何形状的软组织变形及力预测的泛化

中文摘要 AI 辅助

精确的软组织模拟对于外科培训、术前规划和触觉反馈系统至关重要。基于有限元法（FEM）数据训练的学习型替代模型为实时推理提供了有前景的途径，但其可靠性取决于校准良好的本构模型。现有方法既未提供跨刚度水平的模型选择系统指导，也无法跨不同组织刚度或几何形状进行泛化。我们在SOFA Framework中对超弹性本构模型进行全面校准，使用不同刚度的重力加载硅胶梁。以校准后的模拟作为训练数据，我们采用软度条件等变图神经网络，实现跨多种组织类型和未见几何形状的变形与力预测。我们的模型达到亚毫米级平均变形精度，推理时间为0.010秒，同时表明力预测质量与上游校准一致性直接相关。

英文摘要

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.

发表机构

• University of Basel（巴塞尔大学）

机构由 AI 辅助整理，请以论文原文为准。