Year: 2021
Author: Xinliang Liu, Lei Zhang, Shengxin Zhu
Numerical Mathematics: Theory, Methods and Applications, Vol. 14 (2021), Iss. 4 : pp. 862–892
Abstract
We demonstrate the construction of generalized Rough Polyharmonic Splines (GRPS) within the Bayesian framework, in particular, for multiscale PDEs with rough coefficients. The optimal coarse basis can be derived automatically by the randomization of the original PDEs with a proper prior distribution and the conditional expectation given partial information on, for example, edge or first order derivative measurements as shown in this paper. We prove the (quasi)-optimal localization and approximation properties of the obtained bases. The basis with respect to edge measurements has first order convergence rate, while the basis with respect to first order derivative measurements has second order convergence rate. Numerical experiments justify those theoretical results, and in addition, show that edge measurements provide a stabilization effect numerically.
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Journal Article Details
Publisher Name: Global Science Press
Language: English
DOI: https://doi.org/10.4208/nmtma.OA-2021-0100
Numerical Mathematics: Theory, Methods and Applications, Vol. 14 (2021), Iss. 4 : pp. 862–892
Published online: 2021-01
AMS Subject Headings:
Copyright: COPYRIGHT: © Global Science Press
Pages: 31
Keywords: Generalized Rough Polyharmonic Splines multiscale elliptic equation Bayesian numerical homogenization edge measurement derivative measurement.
Author Details
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Subspace decomposition based DNN algorithm for elliptic type multi-scale PDEs
Li, Xi-An
Xu, Zhi-Qin John
Zhang, Lei
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https://doi.org/10.1016/j.jcp.2023.112242 [Citations: 6]