Randomized linear algebra for model reduction. Part I: Galerkin methods and error estimation

TL;DR

This paper introduces a probabilistic approach to reduce the computational cost of model order reduction for parameter-dependent linear equations using randomized sketches, enabling efficient, parallel, and memory-constrained model construction with reliable error estimation.

Contribution

It presents a novel randomized method for constructing reduced order models and estimating errors, improving efficiency and scalability over traditional techniques.

Findings

Reduced models are quasi-optimal with high probability.

Algorithms are highly parallelizable and suitable for streaming environments.

The error estimation method is more efficient and robust against round-off errors.

Abstract

We propose a probabilistic way for reducing the cost of classical projection-based model order reduction methods for parameter-dependent linear equations. A reduced order model is here approximated from its random sketch, which is a set of low-dimensional random projections of the reduced approximation space and the spaces of associated residuals. This approach exploits the fact that the residuals associated with approximations in low-dimensional spaces are also contained in low-dimensional spaces. We provide conditions on the dimension of the random sketch for the resulting reduced order model to be quasi-optimal with high probability. Our approach can be used for reducing both complexity and memory requirements. The provided algorithms are well suited for any modern computational environment. Major operations, except solving linear systems of equations, are embarrassingly parallel. Our version of proper orthogonal decomposition can be computed on multiple workstations with a communication cost independent of the dimension of the full order model. The reduced order model can even be constructed in a so-called streaming environment, i.e., under extreme memory constraints. In addition, we provide an efficient way for estimating the error of the reduced order model, which is not only more efficient than the classical approach but is also less sensitive to round-off errors. Finally, the methodology is validated on benchmark problems.