论文标题
使用广义回归神经网络自动化2D和3D有限元的超透明调整和网状变形
Automated 2D and 3D Finite Element Overclosure Adjustment and Mesh Morphing Using Generalized Regression Neural Networks
论文作者
论文摘要
三维（3D）几何形状的计算机表示对于模拟工程和科学的系统和过程至关重要。在医学，更具体地说，获得和使用3D几何形状对于许多工作流程至关重要。但是，尽管存在许多工具来获得有机结构的3D几何形状，但几乎没有采取任何措施使它们用于其预期的医疗目的。此外，许多提出的工具都是专有的，限制了它们的使用。这项工作介绍了两种基于广义回归神经网络（GRNN）和4个过程以执行网状变形和超透明调整的新算法。实施了这些算法，并使用测试案例来验证它们针对现有算法以证明性能的提高。最终的算法通过转换为基于GRNN的实现，根据径向基础功能（RBF）网络对现有技术的改进进行了改进。这些算法和源代码的MATLAB中的实现在以下位置公开可用： https://github.com/thor-andreassen/femors https://simtk.org/projects/femors-rbf https://www.mathworks.com/matlabcentral/fileexchange/120353-finite-element-morphing-morphing-overclosure-dredauction-reduction-and-and-and-and-and-and-and-stlicing
Computer representations of three-dimensional (3D) geometries are crucial for simulating systems and processes in engineering and science. In medicine, and more specifically, biomechanics and orthopaedics, obtaining and using 3D geometries is critical to many workflows. However, while many tools exist to obtain 3D geometries of organic structures, little has been done to make them usable for their intended medical purposes. Furthermore, many of the proposed tools are proprietary, limiting their use. This work introduces two novel algorithms based on Generalized Regression Neural Networks (GRNN) and 4 processes to perform mesh morphing and overclosure adjustment. These algorithms were implemented, and test cases were used to validate them against existing algorithms to demonstrate improved performance. The resulting algorithms demonstrate improvements to existing techniques based on Radial Basis Function (RBF) networks by converting to GRNN-based implementations. Implementations in MATLAB of these algorithms and the source code are publicly available at the following locations: https://github.com/thor-andreassen/femors https://simtk.org/projects/femors-rbf https://www.mathworks.com/matlabcentral/fileexchange/120353-finite-element-morphing-overclosure-reduction-and-slicing
