Optimal experimental design under irreducible uncertainty for linear inverse problems governed by PDEs

Karina Koval, Alen Alexanderian, Georg Stadler

Research output: Contribution to journalArticlepeer-review

Abstract

We present a method for computing A-optimal sensor placements for infinite-dimensional Bayesian linear inverse problems governed by PDEs with irreducible model uncertainties. Here, irreducible uncertainties refers to uncertainties in the model that exist in addition to the parameters in the inverse problem, and that cannot be reduced through observations. Specifically, given a statistical distribution for the model uncertainties, we compute the optimal design that minimizes the expected value of the posterior covariance trace. The expected value is discretized using Monte Carlo leading to an objective function consisting of a sum of trace operators and a binary-inducing penalty. Minimization of this objective requires a large number of PDE solves in each step. To make this problem computationally tractable, we construct a composite low-rank basis using a randomized range finder algorithm to eliminate forward and adjoint PDE solves. We also present a novel formulation of the A-optimal design objective that requires the trace of an operator in the observation rather than the parameter space. The binary structure is enforced using a weighted regularized ℓ 0-sparsification approach. We present numerical results for inference of the initial condition in a subsurface flow problem with inherent uncertainty in the flow fields and in the initial times.

Original languageEnglish (US)
Article number075007
JournalInverse Problems
Volume36
Issue number7
DOIs
StatePublished - Jul 2020

Keywords

  • inverse problems
  • model reduction
  • model uncertainty
  • optimal design
  • optimization under uncertainty
  • subsurface flow

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Signal Processing
  • Mathematical Physics
  • Computer Science Applications
  • Applied Mathematics

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