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A-posteriori QMC-FEM error estimation for Bayesian inversion and optimal control with entropic risk measure

by M. Longo and Ch. Schwab and A. Stein

(Report number 2023-04)

Abstract
We propose a novel a-posteriori error estimation technique, where the target quantities of interest are ratios of high-dimensional integrals, as occur e.g. in PDE constrained Bayesian inversion and PDE constrained optimal control subject to an entropic risk measure. We consider in particular parametric, elliptic PDEs with affine-parametric diffusion coefficient, on high-dimensional parameter spaces. We combine our recent a-posteriori Quasi-Monte Carlo (QMC) error analysis, with Finite Element a-posteriori error estimation. The proposed approach yields a computable a-posteriori estimator which is reliable, up to higher order terms. The estimator's reliability is uniform with respect to the PDE discretization, and robust with respect to the parametric dimension of the uncertain PDE input.

Keywords: Quasi-Monte Carlo, a-posteriori estimator, Bayesian inverse problem, optimal control, parametric PDEs

@Techreport{LSS23_1041,
  author = {M. Longo and Ch. Schwab and A. Stein},
  title = {A-posteriori QMC-FEM error estimation for Bayesian inversion and optimal control with entropic risk measure},
  institution = {Seminar for Applied Mathematics, ETH Z{\"u}rich},
  number = {2023-04},
  address = {Switzerland},
  url = {https://www.sam.math.ethz.ch/sam_reports/reports_final/reports2023/2023-04.pdf },
  year = {2023}
}

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