Generalized rough polyharmonic splines for multiscale PDEs with rough coefficients

Xinliang Liu, Lei Zhang*, Shengxin Zhu*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)862-892
Number of pages31
JournalNumerical Mathematics
Issue number4
StatePublished - Nov 2021


  • Bayesian numerical homogenization
  • Derivative measurement
  • Edge measurement
  • Generalized Rough Polyharmonic Splines
  • Multiscale elliptic equation

ASJC Scopus subject areas

  • Modeling and Simulation
  • Control and Optimization
  • Computational Mathematics
  • Applied Mathematics


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