WIAS Preprint No. 2897, (2021)

Adaptive non-intrusive reconstruction of solutions to high-dimensional parametric PDEs


Authors

  • Eigel, Martin
    ORCID: 0000-0003-2687-4497
  • Farchmin, Nando
  • Heidenreich, Sebastian
  • Trunschke, Philipp

2020 Mathematics Subject Classification

  • 15A69 62J02 65N15 65N35 65Y20

Keywords

  • Uncertainty quantification, adaptivity, low-rank tensor regression, tensor train, parametric PDEs, residual error estimator, stochastic Galerkin FEM

DOI

10.20347/WIAS.PREPRINT.2897

Abstract

Numerical methods for random parametric PDEs can greatly benefit from adaptive refinement schemes, in particular when functional approximations are computed as in stochastic Galerkin and stochastic collocations methods. This work is concerned with a non-intrusive generalization of the adaptive Galerkin FEM with residual based error estimation. It combines the non-intrusive character of a randomized least-squares method with the a posteriori error analysis of stochastic Galerkin methods. The proposed approach uses the Variational Monte Carlo method to obtain a quasi-optimal low-rank approximation of the Galerkin projection in a highly efficient hierarchical tensor format. We derive an adaptive refinement algorithm which is steered by a reliable error estimator. Opposite to stochastic Galerkin methods, the approach is easily applicable to a wide range of problems, enabling a fully automated adjustment of all discretization parameters. Benchmark examples with affine and (unbounded) lognormal coefficient fields illustrate the performance of the non-intrusive adaptive algorithm, showing best-in-class performance.

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