A nonlinear graph-based theory for dynamical network observability

TL;DR

This paper introduces a graph-based method to identify minimal sets of variables needed for full observability in complex nonlinear dynamical networks, simplifying the measurement process.

Contribution

It proposes a novel approach using pruned fluence graphs derived from the Jacobian to determine observable nodes in nonlinear networks.

Findings

The method accurately identifies observable nodes in high-dimensional reaction networks.

Validation shows the approach's effectiveness in complex nonlinear systems.

The approach simplifies the process of ensuring network observability.

Abstract

A faithful description of the state of a complex dynamical network would require, in principle, the measurement of all its $d$ variables, an infeasible task for systems with practical limited access and composed of many nodes with high dimensional dynamics. However, even if the network dynamics is observable from a reduced set of measured variables, how to reliably identifying such a minimum set of variables providing full observability remains an unsolved problem. From the Jacobian matrix of the governing equations of nonlinear systems, we construct a {\it pruned fluence graph} in which the nodes are the state variables and the links represent {\it only the linear} dynamical interdependences encoded in the Jacobian matrix after ignoring nonlinear relationships. From this graph, we identify the largest connected sub-graphs where there is a path from every node to every other node and there are not outcoming links. In each one of those sub-graphs, at least one node must be measured to correctly monitor the state of the system in a $d$-dimensional reconstructed space. Our procedure is here validated by investigating large-dimensional reaction networks for which the determinant of the observability matrix can be rigorously computed.