Model order reduction and sparse orthogonal expansions for random linear dynamical systems

TL;DR

This paper develops methods for reducing the complexity of random linear dynamical systems by constructing sparse orthogonal expansions, using basis reduction and model order reduction techniques, with proven error bounds and numerical validation.

Contribution

It introduces two novel approaches for sparse representation in stochastic Galerkin systems, combining basis omission and projection-based model order reduction with error analysis.

Findings

Sparse representations significantly reduce computational complexity.

Error bounds are established for both reduction methods.

Numerical experiments validate the effectiveness of the approaches.

Abstract

We consider linear dynamical systems of ordinary differential equations or differential algebraic equations. Physical parameters are substituted by random variables for an uncertainty quantification. We expand the state variables as well as a quantity of interest into an orthogonal system of basis functions, which depend on the random variables. For example, polynomial chaos expansions are applicable. The stochastic Galerkin method yields a larger linear dynamical system, whose solution approximates the unknown coefficients in the expansions. The Hardy norms of the transfer function provide information about the input-output behaviour of the Galerkin system. We investigate two approaches to construct a sparse representation of the quantity of interest, where just a low number of coefficients is non-zero. Firstly, a standard basis is reduced by the omission of basis functions, whose accompanying Hardy norms are relatively small. Secondly, a projection-based model order reduction is applied to the Galerkin system and allows for the definition of new basis functions as a sparse representation. In both cases, we prove error bounds on the sparse approximation with respect to Hardy norms. Numerical experiments are demonstrated for a test example modelling a linear electric circuit.