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Computer representations of three-dimensional (3D) geometries are crucial forsimulating systems and processes in engineering and science. In medicine, andmore specifically, biomechanics and orthopaedics, obtaining and using 3Dgeometries is critical to many workflows. However, while many tools exist toobtain 3D geometries of organic structures, little has been done to make themusable for their intended medical purposes. Furthermore, many of the proposedtools are proprietary, limiting their use. This work introduces two novelalgorithms based on Generalized Regression Neural Networks (GRNN) and 4processes to perform mesh morphing and overclosure adjustment. These algorithmswere implemented, and test cases were used to validate them against existingalgorithms to demonstrate improved performance. The resulting algorithmsdemonstrate improvements to existing techniques based on Radial Basis Function(RBF) networks by converting to GRNN-based implementations. Implementations inMATLAB of these algorithms and the source code are publicly available at thefollowing 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

Automated 2D and 3D Finite Element Overclosure Adjustment and Mesh Morphing Using Generalized Regression Neural Networks