Mean-variance risk-averse optimal control of systems governed by PDEs with random parameter fields using quadratic approximations

Alen Alexanderian, Noemi Petra, Georg Stadler, Omar Ghattas

Research output: Contribution to journalArticle

Abstract

We present a method for optimal control of systems governed by partial differential equations (PDEs) with uncertain parameter fields. We consider an objective function that involves the mean and variance of the control objective, leading to a risk-averse optimal control problem. Conventional numerical methods for optimization under uncertainty are prohibitive when applied to this problem. To make the optimal control problem tractable, we invoke a quadratic Taylor series approximation of the control objective with respect to the uncertain parameter field. This enables deriving explicit expressions for the mean and variance of the control objective in terms of its gradients and Hessians with respect to the uncertain parameter. The risk-averse optimal control problem is then formulated as a PDE-constrained optimization problem with constraints given by the forward and adjoint PDEs defining these gradients and Hessians. The expressions for the mean and variance of the control objective under the quadratic approximation involve the trace of the (preconditioned) Hessian and are thus prohibitive to evaluate. To overcome this difficulty, we employ trace estimators, which only require a modest number of Hessian-vector products. We illustrate our approach with two specific problems: the control of a semilinear elliptic PDE with an uncertain boundary source term, and the control of a linear elliptic PDE with an uncertain coefficient field. For the latter problem, we derive adjoint-based expressions for efficient computation of the gradient of the risk-averse objective with respect to the controls. Along with the quadratic approximation and trace estimation, this ensures that the cost of computing the risk-averse objective and its gradient with respect to the control| measured in the number of PDE solves|is independent of the (discretized) parameter and control dimensions, and depends only on the number of random vectors employed in the trace estimation, leading to an efficient quasi-Newton method for solving the optimal control problem. Finally, we present a comprehensive numerical study of an optimal control problem for fluid flow in a porous medium with an uncertain permeability field.

Original languageEnglish (US)
Pages (from-to)1166-1192
Number of pages27
JournalSIAM-ASA Journal on Uncertainty Quantification
Volume5
Issue number1
DOIs
StatePublished - Jan 1 2017

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Quadratic Approximation
Random Parameters
Partial differential equations
Optimal Control
Partial differential equation
Optimal Control Problem
Uncertain Parameters
Trace
Gradient
Elliptic Partial Differential Equations
Risk-averse
Random parameters
Approximation
Mean-variance
Optimal control
Quasi-Newton Method
Cross product
Linear partial differential equation
Taylor series
Constrained Optimization Problem

Keywords

  • Gaussian measure
  • Hessian
  • Optimal control
  • Optimization under uncertainty
  • PDE-constrained optimization
  • PDEs with random coefficients
  • Risk-aversion
  • Trace estimators

ASJC Scopus subject areas

  • Statistics and Probability
  • Modeling and Simulation
  • Statistics, Probability and Uncertainty
  • Discrete Mathematics and Combinatorics
  • Applied Mathematics

Cite this

Mean-variance risk-averse optimal control of systems governed by PDEs with random parameter fields using quadratic approximations. / Alexanderian, Alen; Petra, Noemi; Stadler, Georg; Ghattas, Omar.

In: SIAM-ASA Journal on Uncertainty Quantification, Vol. 5, No. 1, 01.01.2017, p. 1166-1192.

Research output: Contribution to journalArticle

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