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Model Order Reduction Methods in Computational Uncertainty Quantification

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Handbook of Uncertainty Quantification

Abstract

This work surveys formulation and algorithms for model order reduction (MOR for short) techniques in accelerating computational forward and inverse UQ. Operator equations (comprising elliptic and parabolic partial differential equations (PDEs for short) and boundary integral equations (BIEs for short)) with distributed uncertain input, being an element of an infinite-dimensional, separable Banach space X, are admitted. Using an unconditional basis of X, computational UQ for these equations is reduced to numerical solution of countably parametric operator equations with smooth parameter dependence.

In computational forward UQ, efficiency of MOR is based on recent sparsity results for countably parametric solutions which imply upper bounds on Kolmogorov N-widths of the manifold of (countably) parametric solutions and quantities of interest (QoI for short) with dimension-independent convergence rates. Subspace sequences which realize the N-width convergence rates are obtained by greedy search algorithms in the solution manifold. Heuristic search strategies in parameter space based on finite searches over anisotropic sparse grids render greedy searches in reduced basis construction feasible. Instances of the parametric forward problems which arise in the greedy searches are assumed to be discretized by abstract classes of Petrov–Galerkin (PG for short) discretizations of the parametric operator equation, covering most conforming primal, dual, and mixed finite element methods (FEMs), as well as certain space-time Galerkin schemes for the application problem of interest. Based on the PG discretization, MOR for both linear and nonlinear and affine and nonaffine parametric problems are presented.

Computational inverse UQ for the mentioned operator equations is considered in the Bayesian setting of [M. Dashti and A.M. Stuart: Inverse problems a Bayesian perspective, arXiv:1302.6989v3, this Handbook]. The (countably) parametric Bayesian posterior density inherits, in the absence of concentration effects for small observation noise covariance, the sparsity and N-width bounds of the (countably) parametric manifolds of solution and QoI. This allows, in turn, for the deployment of MOR techniques for the parsimonious approximation of the parametric Bayesian posterior density, with convergence rates which are only limited by the sparsity of the uncertain inputs in the forward model.

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Authors and Affiliations

  1. Institute for Computational Engineering and Sciences, The University of Texas at Austin, 201 East 24th Street, Stop C0200, TX 78712-1229, Austin, TX, USA

    Peng Chen

  2. Departement Mathematik, Seminar for Applied Mathematics, ETH Zurich, Switzerland

    Christoph Schwab

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  1. Peng Chen
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Correspondence to Peng Chen .

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Editors and Affiliations

  1. Department of Civil and Environmental Engineering, University of Southern California, Los Angeles, California, USA

    Roger Ghanem

  2. Social and Decision Analytics Laboratory, Virginia Bioinformatics Institute, Virginia Tech University, Arlington, Virginia, USA

    David Higdon

  3. Computing and Mathematical Sciences, California Institute of Technology, Pasadena, California, USA

    Houman Owhadi

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Chen, P., Schwab, C. (2017). Model Order Reduction Methods in Computational Uncertainty Quantification. In: Ghanem, R., Higdon, D., Owhadi, H. (eds) Handbook of Uncertainty Quantification. Springer, Cham. https://doi.org/10.1007/978-3-319-12385-1_70

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