Low-rank tensor methods for model order reduction

TL;DR

This paper reviews low-rank tensor techniques for creating reduced order models that efficiently approximate complex parameter-dependent functions, aiding in faster evaluations in uncertainty quantification and related fields.

Contribution

It provides a comprehensive overview of low-rank tensor methods for model order reduction, including various approaches and formats for multivariate functions.

Findings

Different low-rank approximation techniques are discussed.

Approaches based on sampling and equations are compared.

Various tensor formats and rank notions are introduced.

Abstract

Parameter-dependent models arise in many contexts such as uncertainty quantification, sensitivity analysis, inverse problems or optimization. Parametric or uncertainty analyses usually require the evaluation of an output of a model for many instances of the input parameters, which may be intractable for complex numerical models. A possible remedy consists in replacing the model by an approximate model with reduced complexity (a so called reduced order model) allowing a fast evaluation of output variables of interest. This chapter provides an overview of low-rank methods for the approximation of functions that are identified either with order-two tensors (for vector-valued functions) or higher-order tensors (for multivariate functions). Different approaches are presented for the computation of low-rank approximations, either based on samples of the function or on the equations that are satisfied by the function, the latter approaches including projection-based model order reduction methods. For multivariate functions, different notions of ranks and the corresponding low-rank approximation formats are introduced.