Sparse polynomial approximation for optimal control problems constrained by elliptic PDEs with lognormal random coefficients
Abstract
In this work, we consider optimal control problems constrained by elliptic partial differential equations (PDEs) with lognormal random coefficients, which are represented by a countably infinite-dimensional random parameter with i.i.d. normal distribution. We approximate the optimal solution by a suitable truncation of its Hermite polynomial chaos expansion, which is known as a sparse polynomial approximation. Based on the convergence analysis in \cite{BachmayrCohenDeVoreEtAl2017} for elliptic PDEs with lognormal random coefficients, we establish the dimension-independent convergence rate of the sparse polynomial approximation of the optimal solution. Moreover, we present a polynomial-based sparse quadrature for the approximation of the expectation of the optimal solution and prove its dimension-independent convergence rate based on the analysis in \cite{Chen2018}. Numerical experiments demonstrate that the convergence of the sparse quadrature error is independent of the active parameter dimensions and can be much faster than that of a Monte Carlo method.
Cite
@article{arxiv.1903.05547,
title = {Sparse polynomial approximation for optimal control problems constrained by elliptic PDEs with lognormal random coefficients},
author = {Peng Chen and Omar Ghattas},
journal= {arXiv preprint arXiv:1903.05547},
year = {2019}
}