Year: 2015
Author: Alexander Bihlo, Ronald D. Haynes, Emily J. Walsh
Journal of Mathematical Study, Vol. 48 (2015), Iss. 2 : pp. 106–124
Abstract
The efficient generation of meshes is an important component in the numerical solution of problems in physics and engineering. Of interest are situations where global mesh quality and a tight coupling to the solution of the physical partial differential equation (PDE) is important. We consider parabolic PDE mesh generation and present a method for the construction of adaptive meshes in two spatial dimensions using stochastic domain decomposition that is suitable for an implementation in a multi- or many-core environment. Methods for mesh generation on periodic domains are also provided. The mesh generator is coupled to a time dependent physical PDE and the system is evolved using an alternating solution procedure. The method uses the stochastic representation of the exact solution of a parabolic linear mesh generator to find the location of an adaptive mesh along the (artificial) subdomain interfaces. The deterministic evaluation of the mesh over each subdomain can then be obtained completely independently using the probabilistically computed solutions as boundary conditions. A small scaling study is provided to demonstrate the parallel performance of this stochastic domain decomposition approach to mesh generation. We demonstrate the approach numerically and compare the mesh obtained with the corresponding single domain mesh using a representative mesh quality measure.
Journal Article Details
Publisher Name: Global Science Press
Language: English
DOI: https://doi.org/10.4208/jms.v48n2.15.02
Journal of Mathematical Study, Vol. 48 (2015), Iss. 2 : pp. 106–124
Published online: 2015-01
AMS Subject Headings:
Copyright: COPYRIGHT: © Global Science Press
Pages: 19
Keywords: Mesh generation Domain decomposition Monte Carlo methods.
Author Details
-
Hybrid PDE solver for data-driven problems and modern branching
BERNAL, FRANCISCO | DOS REIS, GONÇALO | SMITH, GREIGEuropean Journal of Applied Mathematics, Vol. 28 (2017), Iss. 6 P.949
https://doi.org/10.1017/S0956792517000109 [Citations: 5] -
DeepLM: Large-scale Nonlinear Least Squares on Deep Learning Frameworks using Stochastic Domain Decomposition
Huang, Jingwei | Huang, Shan | Sun, Mingwei2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), (2021), P.10303
https://doi.org/10.1109/CVPR46437.2021.01017 [Citations: 5] -
Probabilistic domain decomposition for the solution of the two-dimensional magnetotelluric problem
Bihlo, Alexander | Farquharson, Colin G. | Haynes, Ronald D. | Loredo-Osti, J ConcepciónComputational Geosciences, Vol. 21 (2017), Iss. 1 P.117
https://doi.org/10.1007/s10596-016-9598-8 [Citations: 3]