神经增强的凯尔文让用于实时软组织变形建模
Neural-Augmented Kelvinlet for Real-Time Soft Tissue Deformation Modeling
AI总结：
本文提出一种结合凯尔文让分析先验与大规模FEM数据的神经模拟框架，用于实时软组织变形建模，提升预测精度与物理合理性。
摘要：
准确且高效的软组织相互作用建模对于推进外科模拟、外科机器人和基于模型的外科自动化至关重要。为实现实时延迟，传统有限元法（FEM）求解器常被神经近似替代；然而，单纯用全数据驱动方式训练此类模型而不结合物理先验知识，常导致泛化能力差和物理上不合理的预测。我们提出了一种新颖的物理指导神经模拟框架，能够实现实时预测复杂单手和多手抓取交互下的软组织变形。我们的方法整合了基于凯尔文让的分析先验知识与大规模FEM数据，捕捉线性和非线性组织响应。这种混合设计在各种神经架构中提高了预测精度和物理合理性，同时保持了交互应用所需的低延迟性能。我们在具有标准腹腔镜抓取工具的挑战性外科操作任务上验证了我们的方法，证明了在变形保真度和时间稳定性方面显著优于现有基线。这些结果确立了凯尔文让增强学习作为实时、物理感知软组织模拟在外科AI中的原理化和计算高效范式。
英文摘要：
Accurate and efficient modeling of soft-tissue interactions is fundamental for advancing surgical simulation, surgical robotics, and model-based surgical automation. To achieve real-time latency, classical Finite Element Method (FEM) solvers are often replaced with neural approximations; however, naively training such models in a fully data-driven manner without incorporating physical priors frequently leads to poor generalization and physically implausible predictions. We present a novel physics-informed neural simulation framework that enables real-time prediction of soft-tissue deformations under complex single- and multi-grasper interactions. Our approach integrates Kelvinlet-based analytical priors with large-scale FEM data, capturing both linear and nonlinear tissue responses. This hybrid design improves predictive accuracy and physical plausibility across diverse neural architectures while maintaining the low-latency performance required for interactive applications. We validate our method on challenging surgical manipulation tasks involving standard laparoscopic grasping tools, demonstrating substantial improvements in deformation fidelity and temporal stability over existing baselines. These results establish Kelvinlet-augmented learning as a principled and computationally efficient paradigm for real-time, physics-aware soft-tissue simulation in surgical AI.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
