Reduced-Order Modeling of Large-Scale Network Systems

TL;DR

This paper reviews recent reduced-order modeling techniques for large-scale network systems, focusing on clustering-based network reduction and structure-preserving dynamical reduction methods.

Contribution

It provides a comprehensive overview of recent advances in reduced-order modeling for complex network systems, highlighting clustering and structure-preserving approaches.

Findings

Clustering-based methods effectively reduce network size.

Generalized balanced truncation preserves system structure.

Enhanced understanding of model simplification techniques.

Abstract

Large-scale network systems describe a wide class of complex dynamical systems composed of many interacting subsystems. A large number of subsystems and their high-dimensional dynamics often result in highly complex topology and dynamics, which pose challenges to network management and operation. This chapter provides an overview of reduced-order modeling techniques that are developed recently for simplifying complex dynamical networks. In the first part, clustering-based approaches are reviewed, which aim to reduce the network scale, i.e., find a simplified network with a fewer number of nodes. The second part presents structure-preserving methods based on generalized balanced truncation, which can reduce the dynamics of each subsystem.