A Comparison of Gradient- and Hessian-Based Optimization Methods for Tetrahedral Mesh Quality Improvement

Author(s):  
Shankar Prasad Sastry ◽  
Suzanne M. Shontz
2006 ◽  
Vol 22 (2) ◽  
pp. 61-74 ◽  
Author(s):  
Lori Freitag Diachin ◽  
Patrick Knupp ◽  
Todd Munson ◽  
Suzanne Shontz

Author(s):  
Kiran H. Shivanna ◽  
Srinivas C. Tadepalli ◽  
Vincent A. Magnotta ◽  
Nicole M. Grosland

The finite element method (FEM) is an invaluable tool in the numerical simulation of biological processes. FEM entails discretization of the structure of interest into elements. This discretization process is termed finite element meshing. The validity of the solution obtained is highly dependent on the quality of the mesh used. Mesh quality can decrease with increased complexity of the structure of interest, as is often evident when meshing biologic structures. This necessitated the development/implementation of generalized mesh quality improvement algorithms.


Symmetry ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 895 ◽  
Author(s):  
Junhyeok Choi ◽  
Harrim Kim ◽  
Shankar Prasad Sastry ◽  
Jibum Kim

We propose a novel deviation-based vertex reordering method for 2D mesh quality improvement. We reorder free vertices based on how likely this is to improve the quality of adjacent elements, based on the gradient of the element quality with respect to the vertex location. Specifically, we prioritize the free vertex with large differences between the best and the worst-quality element around the free vertex. Our method performs better than existing vertex reordering methods since it is based on the theory of non-smooth optimization. The downhill simplex method is employed to solve the mesh optimization problem for improving the worst element quality. Numerical results show that the proposed vertex reordering techniques improve both the worst and average element, compared to those with existing vertex reordering techniques.


2012 ◽  
Vol 30 (3) ◽  
pp. 315-329 ◽  
Author(s):  
Shankar P. Sastry ◽  
Suzanne M. Shontz ◽  
Stephen A. Vavasis

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