A model reduction method for multiscale elliptic PDEs with random coefficients using an optimization approach

Thomas Y Hou Dingjiong Ma Zhiwen Zhang

Numerical Analysis and Scientific Computing mathscidoc:1912.431042

Multiscale Modeling & Simulation, 17, (2), 826-853, 2019.6
In this paper, we propose a model reduction method for solving multiscale elliptic PDEs with random coefficients in the multiquery setting using an optimization approach. The optimization approach enables us to construct a set of localized multiscale data-driven stochastic basis functions that give an optimal approximation property of the solution operator. Our method consists of the offline and online stages. In the offline stage, we construct the localized multiscale data-driven stochastic basis functions by solving an optimization problem. In the online stage, using our basis functions, we can efficiently solve multiscale elliptic PDEs with random coefficients with relatively small computational costs. Therefore, our method is very efficient in solving target problems with many different force functions. The convergence analysis of the proposed method is also presented and has been verified by the numerical simulations.
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@inproceedings{thomas2019a,
  title={A model reduction method for multiscale elliptic PDEs with random coefficients using an optimization approach},
  author={Thomas Y Hou, Dingjiong Ma, and Zhiwen Zhang},
  url={http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20191224211431107456606},
  booktitle={Multiscale Modeling & Simulation},
  volume={17},
  number={2},
  pages={826-853},
  year={2019},
}
Thomas Y Hou, Dingjiong Ma, and Zhiwen Zhang. A model reduction method for multiscale elliptic PDEs with random coefficients using an optimization approach. 2019. Vol. 17. In Multiscale Modeling & Simulation. pp.826-853. http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20191224211431107456606.
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