Time-stepping and Krylov methods for large-scale instability problems

TL;DR

This paper reviews matrix-free methods for analyzing large-scale nonlinear dynamical systems, focusing on time-stepping and Krylov techniques to overcome computational and memory limitations.

Contribution

It provides a comprehensive overview of matrix-free approaches for fixed point computations and linear stability analysis in very large-scale systems.

Findings

Matrix-free methods enable stability analysis without explicit Jacobian assembly.

Krylov subspace techniques are effective for large-scale eigenvalue problems.

Time-stepping approaches facilitate the study of nonlinear dynamical systems at scale.

Abstract

With the ever increasing computational power available and the development of high-performances computing, investigating the properties of realistic very large-scale nonlinear dynamical systems has been become reachable. It must be noted however that the memory capabilities of computers increase at a slower rate than their computational capabilities. Consequently, the traditional matrix-forming approaches wherein the Jacobian matrix of the system considered is explicitly assembled become rapidly intractable. Over the past two decades, so-called matrix-free approaches have emerged as an efficient alternative. The aim of this chapter is thus to provide an overview of well-grounded matrix-free methods for fixed points computations and linear stability analyses of very large-scale nonlinear dynamical systems.