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AN EFFICIENT MULTIMODES MONTE CARLO HOMOGENIZATION METHOD FOR RANDOM MATERIALS

delete2022-06-28
delete9
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OA
AI
Y
Yang, Zihao
J
Jizu Huang *
F
Feng, Xiaobing
X
Xiaofei Guan
DOI:10.1137/21M1454237delete
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摘要

摘要

En 中文
In this paper, we propose and analyze a new stochastic homogenization method for diffusion equations with random and fast oscillatory coefficients. In the proposed method, the homogenized solutions are sought through a two-stage procedure. In the first stage, the original oscillatory diffusion equation is approximated, for each fixed random sample omega, by a spatially homogenized diffusion equation with piecewise constant coefficients, resulting in a random diffusion equation. In the second stage, the resulting random diffusion equation is approximated and computed by using an efficient multimodes Monte Carlo method which only requires solving a diffusion equation with a constant diffusion coefficient and a random right-hand side. The main advantage of the proposed method is that it separates the computational difficulty caused by the spatial fast oscillation of the solution and caused by the randomness of the solution, so they can be overcome separately using different strategies. The convergence of the solution of the spatially homogenized equation (from the first stage) to the solution of the original random diffusion equation is established, and the optimal rate of convergence is also obtained for the proposed multimodes Monte Carlo method. Numerical experiments on some benchmark test problems for random composite materials are also presented to gauge the efficiency and accuracy of the proposed two-stage stochastic homogenization method.
Keyword:
stochastic homogenization
multimodes Monte Carlo method
finite element method
convergence and error estimates
random composite materials

期刊

SIAM Journal on Scientific Computing 封面图
SIAM Journal on Scientific Computing
IF:
2.6
论文数:
5.1K
被引数:
1.8W

机构

N
Northwestern Polytechnical University
学者数:
4.6W
论文数: 3.7W
被引数: 5.3W
A
academy of mathematics & system sciences, cas
学者数:
755
论文数: 768
被引数: 0
University of Tennessee System 封面图
University of Tennessee System
学者数:
2.9W
论文数: 2.7W
被引数: 115
C
chinese academy of sciences
学者数:
56.7W
论文数: 45.0W
被引数: 704
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