Random fields representations for stochastic elliptic boundary value problems and statistical inverse problems

TL;DR

This paper develops a new framework for identifying non-Gaussian positive matrix-valued random fields in stochastic elliptic boundary value problems, enabling efficient high-dimensional inverse problem solutions.

Contribution

It introduces a novel class of non-Gaussian positive-definite matrix-valued random fields, along with a minimal parametrization and an efficient identification procedure.

Findings

New class of non-Gaussian random fields introduced

Complete identification procedure proposed

Low-rank tensor methods reduce computational complexity

Abstract

This paper presents new results allowing an unknown non-Gaussian positive matrix-valued random field to be identified through a stochastic elliptic boundary value problem, solving a statistical inverse problem. A new general class of non-Gaussian positive-definite matrix-valued random fields, adapted to the statistical inverse problems in high stochastic dimension for their experimental identification, is introduced and its properties are analyzed. A minimal parametrization of discretized random fields belonging to this general class is proposed. Using this parametrization of the general class, a complete identification procedure is proposed. New results of the mathematical and numerical analyzes of the parameterized stochastic elliptic boundary value problem are presented. The numerical solution of this parametric stochastic problem provides an explicit approximation of the application that maps the parameterized general class of random fields to the corresponding set of random solutions. This approximation can be used during the identification procedure in order to avoid the solution of multiple forward stochastic problems. Since the proposed general class of random fields possibly contains random fields which are not uniformly bounded, a particular mathematical analysis is developed and dedicated approximation methods are introduced. In order to obtain an algorithm for constructing the approximation of a very high-dimensional map, complexity reduction methods are introduced and are based on the use of low-rank approximation methods that exploit the tensor structure of the solution which results from the parametrization of the general class of random fields.